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This study presents a multi-document text summarizing system that employs a hybrid approach, including both extractive and abstractive methods. The goal of document summarizing is to create a coherent and comprehensive summary that captures the essential information contained in the document. The difficulty in multi-document text summarization lies in the lengthy nature of the input material and the potential for redundant information. This study utilises a combination of methods to address this issue. This study uses the TextRank algorithm as an extractor for each document to condense the input sequence. This extractor is designed to retrieve crucial sentences from each document, which are then aggregated and utilised as input for the abstractor. This study uses bidirectional and auto-regressive transformers (BART) as an abstractor. This abstractor serves to condense the primary sentences in each document into a more cohesive summary. The evaluation of this text summarizing system was conducted using the ROUGE measure. The research yields ROUGE R1 and R2 scores of 41.95 and 14.81, respectively. [ABSTRACT FROM AUTHOR]

Keywords are used to provide a concise summary of the text, enabling the quick understanding of core information and assisting in filtering out irrelevant content. In this paper, an improved TextRank keyword extraction algorithm based on word vectors and multi-feature weighting (IWF-TextRank) is proposed to improve the accuracy of keyword extraction by comprehensively considering multiple features of words. The key innovation is demonstrated through the application of a backpropagation neural network, combined with sequential relationship analysis, to calculate the comprehensive weight of words. Additionally, word vectors trained using Word2Vec are utilised to enhance the model's semantic understanding. Finally, the effectiveness of the algorithm is verified from various aspects using traffic accident causation data. The results show that this algorithm demonstrates a significant optimisation effect in keyword extraction. Compared with the traditional model, the IWF-TextRank algorithm shows significant improvement in accuracy (p-value), recall (R-value), and F-value. [ABSTRACT FROM AUTHOR]

This study introduces a hybrid text summarization technique designed to enhance the analysis of qualitative feedback from online educational surveys. The technique was implemented at the Hellenic Open University (HOU) to tackle the challenges of processing large volumes of student feedback. The TextRank and Walktrap algorithms along with GPT-4o mini were used to analyze student comments regarding positive experiences, study challenges, and suggestions for improvement. The results indicate that students are satisfied with tutor–student interactions but concerns were raised about educational content and scheduling issues. To evaluate the proposed summarization approach, the G-Eval and DeepEval summarization metrics were employed, assessing the relevance, coherence, consistency, fluency, alignment, and coverage of the summaries. This research addresses the increasing demand for effective qualitative data analysis in higher education and contributes to ongoing discussions on student feedback in distance learning environments. By effectively summarizing open-ended responses, universities can better understand student experiences and make informed decisions to improve the educational process. [ABSTRACT FROM AUTHOR]

Text summarisation reduces text length while retaining important information, helping individuals, especially students, in managing information overload during research or assignments. However, existing text summarisation methods often lose important details, generate irrelevant or redundant sentences, or produce incoherent summaries. This study introduces a hybrid approach, LongT5Rank (coined in this study), which combines TextRank, an extractive summarisation algorithm, with LongT5, an abstractive summarisation algorithm, to automate the summarisation process. TextRank utilizes GloVe, a pre-trained word embedding model, and PageRank, a graph-based ranking algorithm, to select representative sentences. LongT5, an encoder-decoder transformer model, condenses extracted sentences into a concise and coherent summary, handling input sequences up to 16,384 tokens, for long-range sequence-to-sequence tasks. The LongT5Rank approach has shown significant achievements, including a minimum 60% compression rate, a minimum 0.6 semantic textual similarity score, and an improved F-measure compared to employing TextRank alone. Furthermore, it received positive feedback from Human Level Performance (HLP), underlining the importance of evaluating results directly from human users. This emphasizes the belief that the performance of the proposed solution should be assessed natively by humans. By combining both extractive and abstractive methods, LongT5Rank excels in generating accurate and coherent summaries. [ABSTRACT FROM AUTHOR]

Software development would have to include automated testing to ensure the finished product and performs as intended. However, the process of test case generation and maintenance can be time-consuming and error-prone, especially when manual methods are used. This research proposes a new approach to improve the efficiency and accuracy of automated testing using latent semantic analysis (LSA)-based TextRank (TR) and particle swarm optimization (PSO) algorithms. The study aims to evaluate the effectiveness of these algorithms in generating and optimizing test cases based on requirements analysis. To retrieve key information from the criteria, methods including text classification (TC), named entity recognition (NER), and sentiment analysis (SA) are used to evaluate the text. Test cases are then generated using LSA-based TR for text summarization and PSO for optimization. The aim of this work is to identify any limitations that need to be addressed and to evaluate the overall efficiency and accuracy of automated testing (AT) using proposed algorithms. The results of this research are expected to have important implications for the software industry, helps to improve the overall efficiency and accuracy of AT. The findings could guide future research that led to the creation of more advanced and effective tools for AT. [ABSTRACT FROM AUTHOR]

Classical Chinese poetry has been increasingly popular in recent years, and modeling its topic is quite a promising area of research. Chinese poems have the characteristic of short in length, but traditional topic models perform poorly when faced with short texts due to the text sparsity. Therefore, topic model should be improved to satisfy the scenario of classical Chinese poems. In this paper, a relational background knowledge boosting based topic model (RBKBTM) was proposed to overcome the text sparsity of Chinese poems. We incorporated background information into the model, which expanded the text content from the semantic perspective. The background knowledge was combined using word embedding and TextRank and was then fed into the core computing process. Subsequently, a new sampling formula was derived. Our proposed model was tested on three different tasks using three different datasets. The results demonstrate that the incorporated background knowledge can effectively overcomes text sparsity, improving the performance and effectiveness of the topic model. [ABSTRACT FROM AUTHOR]

A large petrochemical construction project is typically executed by multiple parties, all bound by contract agreement. During the execution phase, issues and problems may arise because the work details are not clearly specified in the contractual agreement. These issues are formally communicated and documented through written correspondence letters. By identifying important keywords within these formal letters, a comprehensive narrative of the project, including its associated issues, can be identified and analyzed. In this research, we introduce an adjusted TextRank algorithm that integrates external features from the Indonesian FastText language model and term frequency-inverse document frequency (TF-IDF) scores to identify important keywords within a dataset of correspondence letters of petrochemical projects. This enhancement involves refining phrase detection, semantic relationship estimation between words, and part-ofspeech (POS) identification for words or phrases. Our results show that the proposed adjustments result in improved evaluation scores compared to the baseline standard TextRank and standard TF-IDF, respectively by 24.1% and 25% in terms of F-1 scores. [ABSTRACT FROM AUTHOR]

As the majority of India's population is multilingual, there is a growing need to develop efficient techniques for summarizing content in various Indian languages. With a significant portion of the population speaking various Indian languages, there will be an increasing demand for efficient techniques to summarize content and voice in these diverse languages. In recent years, Natural Language Processing (NLP) has emerged as a powerful tool for processing and understanding textual data. The digital revolution has revolutionized education, offering an abundance of educational content in Indian languages. However, this vast amount of information can overwhelm learners, making it challenging for them to grasp and retain essential concepts. To address this challenge, our focus is on leveraging the power of NLP to develop a specialized text and voice summarization system for Indian languages, aiming to optimize the learning experience. The challenges posed by Indian languages are they have diverse scripts, different grammar rules, and regional variations. This linguistic understanding will lay the groundwork for designing NLP algorithms that adapt effectively to different languages, ensuring the accuracy and relevance of the summarized content. [ABSTRACT FROM AUTHOR]

In this paper, we construct an academic literature knowledge graph based on the relationship between documents to facilitate the storage and research of academic literature data. Keywords are an important type of node in the knowledge graph. To solve the problem that there are no keywords in some documents for several reasons in the process of knowledge graph construction, an improved keyword extraction algorithm called TP-CoGlo-TextRank is proposed by using word frequency, position, word co-occurrence frequency, and a word embedding model. By combining the word frequency and position in the document, the importance of words is distinguished. By introducing the GloVe word-embedding model, which brings the external knowledge of documents into the TextRank algorithm, and combining the internal word co-occurrence frequency in the documents, the word-adjacency relationship is transferred non-uniformly. Finally, the words with the highest scores are combined into phrases if they are adjacent in the original text. The validity of the TP-CoGlo-TextRank algorithm is verified by experiments. On this basis, the Neo4j graph database is used to store and display the academic literature knowledge graph, to provide data support for research tasks such as text clustering, automatic summarization, and question-answering systems. [ABSTRACT FROM AUTHOR]

Keywords are used to provide a concise summary of the text, enabling the quick understanding of core information and assisting in filtering out irrelevant content. In this paper, an improved TextRank keyword extraction algorithm based on word vectors and multi-feature weighting (IWF-TextRank) is proposed to improve the accuracy of keyword extraction by comprehensively considering multiple features of words. The key innovation is demonstrated through the application of a backpropagation neural network, combined with sequential relationship analysis, to calculate the comprehensive weight of words. Additionally, word vectors trained using Word2Vec are utilised to enhance the model’s semantic understanding. Finally, the effectiveness of the algorithm is verified from various aspects using traffic accident causation data. The results show that this algorithm demonstrates a significant optimisation effect in keyword extraction. Compared with the traditional model, the IWF-TextRank algorithm shows significant improvement in accuracy (p-value), recall (R-value), and F-value.

Traditional news topic classification methods suffer from inaccurate text semantics, sparse text features, and low classification accuracy. Based on this, this paper proposes a news topic classification method based on Enhanced Language Representation with Informative Entities (ERNIE) and multi-feature fusion. A semantically more accurate representation of text embedding is obtained by ERNIE. In addition, this paper extracts word, context, and key sentence based on the news text. The key sentences of the news are obtained through the TextRank algorithm, which enables the model to focus on the content points of the news. Finally, this paper uses the attention mechanism to realize the fusion of multiple features. The proposed method is experimented on BBC News. The experimental results show that we achieve classification accuracies superior to those of the compared methods, while validating the structural validity of the proposed method. The method in this paper has a positive effect on promoting the research of news topic classification. [ABSTRACT FROM AUTHOR]

In this work, sentence similarity between sentences of software bug report is computed. For this, two methods are utilized, Latent Semantic Analysis and Text Rank. Latent Semantic Analysis is used to compute semantic similarity between sentences of bug reports which infers deeper and hidden relation between words. From this, a pair of sentences with semantic similarity above a set threshold is selected and from one pair of sentences, only one sentence is selected. The remaining sentences are passed into TextRank algorithm and sentences with high similarity are further selected to generate a coherent summary. The proposed approach is evaluated on a newly constructed Apache Project Bug Report Corpus and existing Bug Report Corpus. The proposed approach is also compared with baseline approaches that mainly focus on only lexical similarity. The results when evaluated on Apache project Bug Report Corpus attains an average value of 80%, 72.57%, 76.05% and 76.57% in terms of precision, recall, F-score and pyramid precision respectively. [ABSTRACT FROM AUTHOR]

Copyright of Journal of East China University of Science & Technology is the property of Journal of East China University of Science & Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Expanding traditional video metadata and recommendation systems encompasses challenges that are difficult to address with conventional methodologies. Limitations in utilizing diverse information when extracting video metadata, along with persistent issues like bias, cold start problems, and the filter bubble effect in recommendation systems, are primary causes of performance degradation. Therefore, a new recommendation system that integrates high-quality video metadata extraction with existing recommendation systems is necessary. This research proposes the “Extraction of Meta-Data for Recommendation using keyword mapping,” which involves constructing contextualized data through object detection models and STT (Speech-to-Text) models, extracting keywords, mapping with the public dataset MovieLens, and applying a Hybrid recommendation system. The process of building contextualized data utilizes YOLO and Google’s Speech-to-Text API. Following this, keywords are extracted using the TextRank algorithm and mapped to the MovieLens dataset. Finally, it is applied to a Hybrid Recommendation System. This paper validates the superiority of this approach by comparing it with the performance of the MovieLens recommendation system that does not expand metadata. Additionally, the effectiveness of metadata expansion is demonstrated through performance comparisons with existing deep learning-based keyword extraction models. Ultimately, this research resolves the cold start and long-tail problems of existing recommendation systems through the construction of video metadata and keyword extraction.

The traditional TextRank algorithm only considers the similarity between sentences but neglects the similarity between articles themselves when generating summaries, and the generated summaries often contain repeated expressions of information. Therefore, a TextRank algorithm based on cooccurrence keywords is proposed. The article is represented as a sentence vector by word2Vc model. Considering the category of the article, the co-occurrence keywords of this kind of article are taken as parameters to participate in the iterative calculation of sentence weight. The sentence weight obtained by iteration is corrected by sentence length, keyword number and other information. The experimental results show that the proposed algorithm can improve the comprehensiveness and accuracy of the summary generation. At the same time, this algorithm uses MMR to remove the redundancy of abstracts, which improves the problem of repeated representation of abstracts. [ABSTRACT FROM AUTHOR]

This study focuses on the problem of efficient classification of university English teaching resources, aiming to improve the ease of browsing and utilization of resources. To address the challenges of existing resource classification, the study proposes a label generation classification algorithm with multi-feature fusion. The algorithm incorporates TF-IDF and location information weights based on TextRank to generate labels containing corpus and location information. The performance of the algorithm is tested and simulation experiments are conducted. The study results show that the mean value of the label generation classification algorithm after multi-feature fusion reaches 0.234, 0.6219, and 0.3632 in terms of accuracy, recall, and F-value, respectively. In the simulation experiments, the algorithm achieves a classification speed of 90.33% faster than that of the traditional single-feature classification algorithm, and the classification error rate is no more than 10%. The label generation classification algorithm has a significant advantage in overall performance and effectively solves the problem of classifying university English teaching resources. This finding is of great significance for improving the utilization of teaching resources and optimizing the management of teaching resources.

The length of a news article may influence people’s interest to read the article. In this case, text summarization can help to create a shorter representative version of an article to reduce people’s read time. This paper proposes to use weighted word embedding based on Word2Vec, FastText, and bidirectional encoder representations from transformers (BERT) models to enhance the TextRank summarization algorithm. The use of weighted word embedding is aimed to create better sentence representation, in order to produce more accurate summaries. The results show that using (unweighted) word embedding significantly improves the performance of the TextRank algorithm, with the best performance gained by the summarization system using BERT word embedding. When each word embedding is weighed using term frequency-inverse document frequency (TF-IDF), the performance for all systems using unweighted word embedding further significantly improve, with the biggest improvement achieved by the systems using Word2Vec (with 6.80% to 12.92% increase) and FastText (with 7.04% to 12.78% increase). Overall, our systems using weighted word embedding can outperform the TextRank method by up to 17.33% in ROUGE-1 and 30.01% in ROUGE-2. This demonstrates the effectiveness of weighted word embedding in the TextRank algorithm for text summarization. [ABSTRACT FROM AUTHOR]

Text summarization techniques offer a way to address the significant challenges faced by clinicians and researchers due to the exponential growth of information in healthcare on the internet. By condensing lengthy text into concise summaries, these techniques facilitate faster, easier, and convenient access to relevant information. This is particularly beneficial in use cases such as online user feedback/reviews about drugs, where valuable insights can be obtained that extend beyond clinical trials and observational studies. This paper comprehensively evaluates six widely used text summarization techniques (LSA, Luhn's Method, Text Rank, T5 Transformer, and Kullback-Leibler, BERT) in extracting key insights, themes and patterns about drugs from online drug reviews. The evaluation considers both quantitative and qualitative aspects, focusing on their applicability to the challenging medical terminology, which is known for its inherent intricacies and complexities. The findings of this study showed the performance of text summarization techniques using metrics such as F1 score, Recall, and Precision, focused on the unigram, bigram, and trigram overlap between the generated text summaries and the reference summaries, utilizing the ROUGE-1, ROUGE-2, and ROUGE-L evaluation methods. It is shown that results showed TextRank to be the most effective text summarization method followed by BERT when working with Medical Terminology in Healthcare & Biomedical Informatics, given its complex hierarchy and extensive vocabulary of medical terms. [ABSTRACT FROM AUTHOR]

Increasing insurance fraud has resulted in the loss of large amounts of money, making it difficult to expand insurance coverage and scale. This phenomenon is particularly acute in the field of health insurance. Medical insurance fraud is the falsification of medical records to obtain medical insurance funds or medical insurance benefits. Therefore, effective detection of health insurance fraud is of great importance for the rational use of health insurance funds. To address the frequent violations and frauds in health insurance, this paper proposes a keyword combination-based approach for health insurance fraud detection. First, a medical dictionary is built by TextRank to segment the surgical procedure text and extract the surgical keywords, then, the synonyms corresponding to each keyword are extracted from the electronic medical record data to form a keyword combination pattern as the final detection rule, and finally, a medical insurance fraud detection model is built on this basis. In this paper, data on acute myocardial infarction and unstable angina were selected for examination, with 1371 and 1787 cases, respectively. The performance of the model was evaluated by the coverage rate and compared experimentally with the TF-IDF and LDA algorithms. The experiments also prove the efficiency and advancedness of the algorithm in this paper. In the case of acute myocardial infarction, the method in this paper improved the coverage rate by 23.77% and 9.4% compared with the TF-IDF and LDA methods respectively. In the case of unstable angina, the coverage of the method in this paper was improved by 20.21% compared to both TF-IDF and LDA methods. [ABSTRACT FROM AUTHOR]

Journal editors are putting a lot of effort into selecting appropriate reviewers for fair and reliable peer review of submitted manuscripts. Editors consider whether the reviewers have no affinity with any of the authors of manuscripts and have sufficient expertise in reviewing the manuscripts. The affinity can be evaluated by whether any of the reviewers has been a coauthor and/or a coworker in a common institution with any of the authors of the manuscript. The expertise depends on the similarity of the research topic between the reviewer’s published papers and the submitted manuscripts. In this paper we propose an algorithm to recommend appropriate reviewers to editors, based on the assessment of these scholarly activities and achievements. To implement this algorithm, TextRank and GenSim library are used to extract feature sets from abstract and introduction sections of both submitted manuscripts and the reviewer candidates’ papers. Based on the extracted feature sets, affinity and expertise check are implemented. An experiment has been conducted with a data set of over 1,000 papers in the field of DB research to evaluate the performance of this algorithm. The experiment consists of affinity check by using 2-mode network matrix operations and expertise check based on Max Similarity and/or topic clustering. Experimental results show that the recommendation algorithm is reasonable on the basis of scholarly activity assessment and achievements. Our method is designed to optimize the reviewer pool for each journal, client and this algorithm is designed with an open license and has the advantage of being free to use and operate. In addition, our algorithm has the advantage of journal-specific optimization by designing to adjust the expertise of the reviewer selection and the weights for exclusion of interests to reflect journal-specific policies.

With the rapid development of network technology, a large amount of information fills the network world, and the performance of the current information extraction model to extract keyword information from a large number of data is insufficient. To solve the problem of insufficient extraction performance in traditional information extraction models, this paper combines text sorting algorithms with document topic generation models. A keyword information extraction model that combines the advantages of the two algorithms is proposed. The performance comparison experiment of this fusion algorithm shows that its accuracy and recall rates are 76.1% and 77.0%, respectively, which outperform the comparing algorithm. In the empirical analysis results of the information extraction model, it is found that the accuracy and precision rates of the proposed information extraction model are 80.16% and 77.54%, respectively, which are better than the comparing model. The proposed model of information extraction is of great importance for the development of the field of information extraction.

Copyright of Journal of Guangxi Normal University - Natural Science Edition is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We present a keyphrase extraction algorithm named TopicLPRank in this paper, which is an improved TopicRank algorithm. Different from the TopicRank which only uses the relative distance information of the text, we think that the length and absolute position of the text candidate keyphrases also have a certain influence on the results of the model for extraction keyphrases. Therefore, the proposed TopicLPRank incorporates these two factors on the basis of the TopicRank. The experimental results show that adding the location information and length information of candidate keyphrases can, respectively, increase the F-Score of the model by around 2.7 % points and 1.7 % points, which is equivalent to an increase of 19.6 and 12.3 % compared with the TopicRank. At the same time, the fusion of the length and location information of the candidate keyphrase can increase the F-Score by around 3.5 percentage points, which is equivalent to an increase of 25.21 % compared with the TopicRank in the dataset NUS. [ABSTRACT FROM AUTHOR]

In the era of big data, people benefit from the existence of tremendous amounts of information. However, availability of said information may pose great challenges. For instance, one big challenge is how to extract useful yet succinct information in an automated fashion. As one of the first few efforts, keyword extraction methods summarize an article by identifying a list of keywords. Many existing keyword extraction methods focus on the unsupervised setting, with all keywords assumed unknown. In reality, a (small) subset of the keywords may be available for a particular article. To use such information, we propose a rigorous probabilistic model based on a semisupervised setup. Our method incorporates the graph-based information of an article into a Bayesian framework via an informative prior so that our model facilitates formal statistical inference, which is often absent from existing methods. To overcome the difficulty arising from high-dimensional posterior sampling, we develop two Markov chain Monte Carlo algorithms based on Gibbs samplers and compare their performance using benchmark data. We use a false discovery rate (FDR)-based approach for selecting the number of keywords, whereas the existing methods use ad hoc threshold values. Our numerical results show that the proposed method compared favorably with state-of-the-art methods for keyword extraction. History: Accepted by Ramaswamy Ramesh, Area Editor for Data Science and Machine Learning. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.1283) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2021.0234) at (http://dx.doi.org/10.5281/zenodo.7348935). [ABSTRACT FROM AUTHOR]

In this study, based on a morpheme segmentation framework, we researched a text keyword extraction method for Uyghur, Kazakh and Kirghiz languages, which have similar grammatical and lexical structures. In these languages, affixes and a stem are joined together to form a word. A stem is a word particle with a notional meaning, while the affixes perform grammatical functions. Because of these derivative properties, the vocabularies used for these languages are huge. Therefore, pre-processing is a necessary step in NLP tasks for Uyghur, Kazakh and Kirghiz. Morpheme segmentation enabled us to remove the suffixes as the auxiliary unit while retaining the meaningful stem and it reduced the dimension of the feature space present in the keyword extraction task for Uyghur, Kazakh and Kirghiz texts. We transformed the morpheme segmentation task into the problem of labeling the morpheme sequences, and we used the Bi-LSTM network to bidirectionally obtain the position feature information of character sequences. We applied CRF to effectively learn the information of the preceding and following label sequences to build a highly accurate Bi-LSTM_CRF morpheme segmentation model, and we prepared morpheme-based experimental text sets by using this model. Subsequently, we used the stem vectors' similarity to modify the TextRank algorithm, subsequent to the training of the stem embedding vector using the Doc2vec algorithm, and then we performed a text keyword extraction experiment. In this experiment, the highest F1 scores of 43.8%, 44% and 43.9% were obtained for three datasets. The experimental results show that the morpheme-based approach provides much better results than the word-based approach, which shows the stem vector similarity weighting is an efficient method for the text keyword extraction task, thus proving the efficiency of morpheme sequence for morphologically derivative languages. [ABSTRACT FROM AUTHOR]

The research about automatic text summarization is common in English text. According to the previous study, automatic text summarization in Bahasa Indonesia is still challenging due to research in this area, especially the research which discusses TextRank algorithm performance, which is still meagerly. Accordingly, this research observes the performance of the TextRank algorithm to summarize the text in Bahasa Indonesia. The TextRank algorithm summarizes a text by sorting out the essential words and relevant sentences regardless of the source language. This algorithm uses a vertex to represent a word. The similarity measurement process will calculate the overlapping words (the same word between two vertices). These overlapping words are represented by the edge, which connects the vertices. Thus, the text forms a graph. This research focuses on the similarity measurement process to determine relevant sentences in a text. As the similarity measurement is critical for the summarization result, this research switches the original process to the Levenshtein Distance algorithm and observes its performance. This research uses the human-produced summarized text by the expert in Bahasa Indonesia linguistics to evaluate the result. The evaluation method is conducted by using ROUGE-1 and ROUGE-2. The result shows that the average of ROUGE-1 and ROUGE-2 for the TextRank algorithm is 0.439 and 0.3186, respectively. Meanwhile, the modified TextRank obtains 0.3999 and 0.2805, respectively. Both of the algorithms have not shown satisfactory results as expected. [ABSTRACT FROM AUTHOR]

Cross-Lingual Summarizer develops a gist of the extract written in English in the National Language of India Hindi. This helps non-anglophonic people to understand what the text says in Hindi. The extractive method of summarization is being used in this paper for summarizing the article. The summary generated in English is then translated into Hindi and made available for Hindi Readers. The Hindi readers get the heart of the article they want to read. Due to the Internet's explosive growth, access to a vast amount of information is now efficient but getting harder and harder. An approach to text extraction summarization that captures the aboutness of the text document was discussed in this paper. One of the many uses for natural language processing (NLP) that significantly affects our daily lives is text summarization. Who has the time to read through complete articles, documents, or books to determine whether they are helpful with the expansion of digital media and the profusion of articles published? The technique was created using TextRank, which was determined using the idea of PageRank established for each page on a website. The presented approach builds a graph with sentences as nodes and the weight of the edge connecting two sentences as its nodes. Modified inverse sentence-cosine frequency similarity gives different words in a sentence different weights. The success of the procedure is demonstrated by the performance evaluation that supported the summary technique. [ABSTRACT FROM AUTHOR]

The quantity of textual data on the internet is growing exponentially, and it is very tough task to obtain important and relevant information from it. An efficient and effective method is required that provides a concise summary of an article. This can be achieved by the usage of automatic text summarization. In this research, the authors suggested an efficient approach for text summarization where an extractive summary is generated from an article. The methodology was modified by integrating a normalized similarity matrix of both BM25+ and conventional TextRank algorithm, which resulted in the improvised results. A graph is generated by taking the sentences in the article as nodes and edge weights as the similarity score between two sentences. The maximum rank nodes are selected, and the summary is extracted. Empirical evaluation of the proposed methodology was analyzed and compared with baseline methods viz. the conventional TextRank algorithm, term frequency–inverse document frequency (TF–IDF) cosine, longest common consequence (LCS), and BM25+ by taking precision, recall, and F1 score as evaluation criteria. ROUGE-1, ROUGE-2, and ROUGE-L scores were calculated for all the methods. The outcomes demonstrate that the proposed method can efficiently summarize any article irrespective of the category it belongs to. [ABSTRACT FROM AUTHOR]

As an important supporting technology for applications such as knowledge base construction and information retrieval, entity disambiguation plays an important role in the field of Natural Language Processing (NLP). However, in the short text environment, it is difficult for entity disambiguation to extract sufficient context features for disambiguation. Aiming at the characteristics of short texts, this paper proposes a disambiguation method of graph models based on entity topic relations. This method uses TextRank algorithm to infer the topic of corpus constructed by knowledge base information, and uses the result of topic inference as the representation of relationship between entities. By combining the disambiguation score given by the semantic matching model based on BERT, the disambiguation network graph is constructed, and the final disambiguation result is obtained through search and sorting. The data set provided in the short text entity link task of CCKS2020 is used to evaluate the method. The experimental results show that the proposed method is better than other entity linking methods in entity disambiguation of short text, and can effectively solve the entity disambiguation problem of Chinese short text. [ABSTRACT FROM AUTHOR]