Search

Text summarization is the process of converting the input document into a short form, provided that it preserves the overall meaning associated with it. Primarily, text summarization is achieved in two ways, i.e., abstractive and extractive. Extractive summarizers select a few best sentences out of the input document, while abstractive methods may modify the sentence structure or introduce new sentences. The proposed approach is an extractive text summarization technique, where we have expanded topic modeling specifically to be applied to multiple lower-level specialized entities (i.e., groups) embedded in a single document. Our goal is to overcome the lack of coherence issues found in the summarization techniques. Topic modeling was initially proposed to model text data at the multi-document and word levels without considering sentence modeling. Subsequently, it has been applied at the sentence level and used for the document summarization; however, certain limitations were associated. Topic modeling does not perform as expected when applied to a single document at the sentence level. To address this shortcoming, we have proposed a summarization approach that is incorporated at the individual document and clusters level (instead of the sentence level). We aim to choose the best statement from each group (containing sentences of the same kind) found in the given text. We have tried to select the perfect topic by evaluating the probability distribution of the words and respective topics' at the cluster level. The method is evaluated on two standard datasets and shows significant performance gains over existing text summarization techniques. Compared to other text summarization techniques, the Rouge parameters for automatic evaluation show a considerable improvement in F-measure, precision, and recall of the generated summary. Furthermore, a manual evaluation has demonstrated that the proposed approach outperforms the current state-of-the-art text summarization approaches. [ABSTRACT FROM AUTHOR]

The difficulty of deriving value out of vast available scientific literature in a condensed form lead us to look for a proficient theme based summarization solution which can preserve precise biomedical content. The study targets to analyze impact of combining semantic biomedical concepts extraction, frequent item-set mining and clustering techniques over information retention, objective functions and ROUGE values for the obtained final summary. The suggested frequent item-set mining and clustering (FRI-CL) graph-based framework uses UMLS metathesarus and BERT-based semantic embeddings to identify domain-relevant concepts. The scrutinized concepts are mined according to their relationship with neighbors and frequency via an amended FP-Growth model. The framework utilizes S-DPMM clustering, which is a probabilistic mixture model and aids in the identification and clubbing of complex relevant patterns to increase coverage of important sub-themes. The sentences with the frequent concepts are scored via PageRank to form an efficient and compelling summary. The research experiments on the 100 sample biomedical documents taken from PubMed archives are evaluated via calculation of ROUGE scores, coverage, readability, non-redundancy, memory utilization and information retention from the summary output. The results with the FRI-CL summarization system showcased 10% ROUGE performance improvement and are at par with the other baseline methods. On an average 30–40% improvement in memory utilization is observed with up to 50% information retention when experiments are performed using S-DPMM clustering. The research indicates that the fusion of semantic mapping, clustering, along with frequent-item set mining of biomedical concepts enhance the overall co-related information covering all sub-themes. [ABSTRACT FROM AUTHOR]

The exponential growth in the number of scientific articles has made it difficult for the researchers to keep themselves updated with the new developments. Scientific document summarization solves this problem by providing a summary of essential contributions. In this paper, we have presented a novel method of scientific document summarization using a multi-objective differential evolution technique. Here, firstly distinct important sentences are extracted by using citation contextualization. These sentences are further clustered using the concept of multi-objective clustering. Two objective functions, PBM index, and XB index, measuring the compactness and separation of sentence clusters, are simultaneously optimized utilizing the search capability of multi-objective differential evolution. We have conducted our experiments on CL-SciSumm 2016, CL-SciSumm 2017, CL-SciSumm 2018, and CL-SciSumm 2019 datasets. Obtained results of CL-SciSumm 2016 and CL-SciSumm 2017 are compared with the state-of-the-art methods. Evaluation results demonstrate that our method outperforms others in terms of ROUGE-2, ROUGE-3, and ROUGE-SU4 scores. [ABSTRACT FROM AUTHOR]

The availability of various digital sources has created a demand for text mining mechanisms. Effective summary generation mechanisms are needed in order to utilize relevant information from often overwhelming digital data sources. In this view, this paper conducts a survey of various single as well as multi-document text summarization techniques. It also provides analysis of treating a query sentence as a common one, segmented from documents for text summarization. Experimental results show the degree of effectiveness in text summarization over different clustering algorithms.

Text summarization is an application of information retrieval where short and non-redundant version of comparatively large text is presented to the end user. In this paper a hybrid approach is presented to generate abstract summary in which sentences are clustered using sentence level relationships among sentences in association with Markov clustering principle. Then sentence ranking is done in each cluster and if possible the top weighted sentence of each cluster is fused using some linguistic rules with its best-fit sentence(if found) within that cluster to generate a new sentence. Then top ranked sentences from each cluster are compressed using classification technique to generate the abstract summary. The proposed system is evaluated with DUC 2002 data set and the result is performing better than other existing systems. [ABSTRACT FROM AUTHOR]

The availability of various digital sources has created a demand for text mining mechanisms. Effective summary generation mechanisms are needed in order to utilize relevant information from often overwhelming digital data sources. In this view, this paper conducts a survey of various single as well as multi-document text summarization techniques. It also provides analysis of treating a query sentence as a common one, segmented from documents for text summarization. Experimental results show the degree of effectiveness in text summarization over different clustering algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we present Latent Drichlet Allocation in automatic text summarization to improve accuracy in document clustering. The experiments involving 398 data set from public blog article obtained by using python scrapy crawler and scraper. Several steps of clustering in this research are preprocessing, automatic document compression using feature method, automatic document compression using LDA, word weighting and clustering algorithm The results show that automatic document summarization with LDA reaches 72% in LDA 40%, compared to traditional k-means method which only reaches 66%. [ABSTRACT FROM AUTHOR]

Automatic Text Summarization (ATS) is an essential field in natural language processing that attempts to condense large text documents so that users can assimilate information quickly. It finds uses in medical document summarization, review generation, and opinion mining. This work investigated an unsupervised extractive summarization approach that combined clustering with topic modeling to reduce topic bias. Latent Dirichlet Allocation was used for topic modeling, while K-Medoids clustering was employed for summary generation. The approach was evaluated on three datasets—Wikihow, CNN/DailyMail, and the DUC2002 Corpus. The Recall Oriented-Understudy for Gisting Evaluation (ROUGE) metrics were used for comparative analysis against recently reported techniques, specifically ROUGE-1 (R-1), ROUGE-2 (R-2), and ROUGE-L (R-L). The suggested framework offered scores of 34.80%, 9.13%, and 32.30% on the Wikihow Dataset, 43.90%, 19.01%, and 41.50% on the CNN/DailyMail Dataset, and 49.35%, 31.53%, and 41.72% on the DUC2002 Corpus (R-1, R-2, R-L respectively). These reported metrics are found to be superior when compared to similar recent works. Further, execution time of the proposed method was also recorded and compared with counterparts, which established its superior speed. Based on these promising outcomes, it was concluded that an unsupervised extractive summarization approach with greater subtopic focus significantly improves over generic topic modeling semantic and deep learning approaches. [ABSTRACT FROM AUTHOR]

• A deep bidirectional language model is used to capture the context of sentences. • The shared content between sentences is quantified using contextualized embeddings. • A hierarchical clustering algorithm is utilized to identify the most relevant sentences. • The summarizer improves the performance of biomedical text summarization. • Contextualized embeddings can effectively capture the context in biomedical summarization. Capturing the context of text is a challenging task in biomedical text summarization. The objective of this research is to show how contextualized embeddings produced by a deep bidirectional language model can be utilized to quantify the informative content of sentences in biomedical text summarization. We propose a novel summarization method that utilizes contextualized embeddings generated by the Bidirectional Encoder Representations from Transformers (BERT) model, a deep learning model that recently demonstrated state-of-the-art results in several natural language processing tasks. We combine different versions of BERT with a clustering method to identify the most relevant and informative sentences of input documents. Using the ROUGE toolkit, we evaluate the summarizer against several methods previously described in literature. The summarizer obtains state-of-the-art results and significantly improves the performance of biomedical text summarization in comparison to a set of domain-specific and domain-independent methods. The largest language model not specifically pretrained on biomedical text outperformed other models. However, among language models of the same size, the one further pretrained on biomedical text obtained best results. We demonstrate that a hybrid system combining a deep bidirectional language model and a clustering method yields state-of-the-art results without requiring labor-intensive creation of annotated features or knowledge bases or computationally demanding domain-specific pretraining. This study provides a starting point towards investigating deep contextualized language models for biomedical text summarization. [ABSTRACT FROM AUTHOR]