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The process of summarization is taking a longer piece of material and reducing it to a shorter version without losing any of the essential information or meaning. Due to the exponential increase in available information and data, automatic text summarizing has emerged as a useful alternative to the time-consuming and error-prone process of manual summation of massive amounts of texts. The summaries the algorithm generates help users better understand the material presented in the original document. There are two main types of summarization: abstract and extractive. The number of available automatic summarization tools for Indian languages is low. Our focus in this area has been on creating an automatic English-language text summarizer utilizing the T5 transformer model; we've employed a manual dataset for testing and training. Here we have used news summary dataset. [ABSTRACT FROM AUTHOR]

Text summarization is the task of reducing the amount of text from a document while keeping the meaning unchanged. Previously statistical extractive metrics have been used to rank summaries however the current summarization models use an abstractive approach. In an abstractive summarization model the main idea from the original document is taken and it is used to generate the summary. Hence in such cases an abstractive measure of comparison is hence needed. An abstractive measure will give better results over extractive measures when contextual meaning of the text bodies are being considered. In this paper a new approach for abstractive text summarization evaluation is proposed which is based on BERT embeddings and Principal Component Analysis (PCA). The pre-trained BERT model is used to generate embeddings for each sentence in the input text, after which PCA is applied for reducing the dimensionality of the embeddings while preserving the most informative features. The L2 distance metric, which is basically Euclidean distance, is used for comparing the similarity of the reduced dimensional embedding vectors. The proposed approach outperforms the ROUGE metric and shows higher scores for summaries which are contextually similar rather than being textually similar. This metric is used to select sentences from the document which is being summarized to create an improved prompt for state-of-the-art text summarization models, like T5 and GPT, to generate more informative summaries. The prompt based generated summary is validated using ROUGE and METEOR metrics. [ABSTRACT FROM AUTHOR]

Bidirectional Encoder Representations from Transformers (BERT) is one method technique in machine learning for solving various natural language processing (NLP) problems, including summarization. One general problem using BERT is getting much time and resources to train the model. This paper aims to implement the small architecture BERT, called Small2BERT, which is expected to summarize the data. Therefore, this study compares pre-trained Small2BERT and Summarization using Word Frequency (SWF) in extracting text summaries. This research uses the dataset of Indian News Summary, which includes news articles from the Hindu, Indian Times, and Guardian from February to August 2017. Small2BERT surpasses SWF in precision, whereas SWF gives a higher score in overall high f1-score. Small2BERT earns a 0.307 f1-score in Rogue-1, compared to 0.35 for SWF. Small2BERT scores 0.19 and 0.304 while SWF achieves 0.304 and 0.31 in f1-score for Rogue-2 and Rogue-L, respectively. [ABSTRACT FROM AUTHOR]

From numerous sources, a vast volume of text data has been developing daily. This enormous volume of data contains crucial knowledge and information that must be skillfully condensed to be usable. In this era of digitalization, vast information is available to us, which are being uploaded online from all over the world in the form of the web pages, and social media etc. To expand quick knowledge from this massive content, the summary of text can help understand the concept fast. Whenever we talk about the text summarization in research article or paper, we must be aware that this colossal amount of data can be generic or scientific and with this we will have an astronomical number of publications. There would be great support to the researchers if the approach of automatically summarization were applied on the scientific articles to increase the speed of understanding. Due of its applicability in numerous fields, automatic summarization of natural language is a prominent academic and industrial focus in computer science. While doing text summarization the essence or the context of the text remains same this could be the primary requirement. It is due to the high usage of Internet as a medium text summarization is developed as an outstanding tool for interpreting text information. Extraction of important information from the source text material is known as text summarization. In this procedure, the extracted data is produced as a concise report and sent to the user as a succinct summary. Broadly text summarization is classified into two methods extractive and abstractive techniques for summarization. Defining a rank from the text file based on the word and features of the sentence, collate them to develop a summary where feature of sentences will be notified based on the statistical and linguistic trait, which we name as extractive summarization method. In other case first we have to understand the details and main points of the documents, after which we need to define these concept in terms of natural language is defined as Abstractive Summarization method. This paper will give you the comparative study of various Abstractive Summarization methods. [ABSTRACT FROM AUTHOR]

Automatic summarization of articles in this big data era is essential to help extract or abstract the most critical points out in a short period. A previous comparative study of extractive algorithms shows that Sukma_Textrank gives the best F score and lowest mean among SMMRY and TextSummarizer for 30 BBC articles. Thus, in this study, Sukma_Textrank was employed to build an automatic summarized application using the natural language processing method, python, HTML and PHP codes. Three linguistics articles were summarized using the built App and compared with SMMRY and TextSummarizer using ROUGE 2.0. Again, it shows that the developed App using Sukma_Textrank outperformed the other two extractive algorithms. [ABSTRACT FROM AUTHOR]

In social media, Twitter has been turned into a convenient platform for online users for creating and sharing information based on recent trends. The short and informal Tweets, which may be used as source of news and also of public opinions regarding recent trends, are huge in volume and numbers. Thus, the challenge to extract reliable and useful information out of enormous Tweets leads to a research avenue, popularly known as Twitter summarization. In this work, we have gathered sequence of tweets corresponding to COVID-19 that carries sentiment and emotion of the people with respect to vaccinations like its utility, side effects etc. Automatic summarization algorithms might help to produce precise reports with respect to an event from a collection of tweets. The present work will predict which summarization algorithm might be chosen and generates the best summary as a decision according to various evaluation features given by the user, and how sentiment analysis of tweets can help improve the result. In this study we find that filtering tweets via sentiment analysis significantly improves the performance of summarization by an approx. 9.47 times. [ABSTRACT FROM AUTHOR]

Query-focused summarization is a variant of automatic summarization that uses a given query to generate summaries. It is used, for example, to generate search result snippets. Although conventional score-based methods tend to generate summaries that contain words in the given query, they offer no guarantees of that. We show that such guarantees can be formulated as an optimization problem where one constraint is that the generated summary contains at least some of the words in the query. We also show that the formulated optimization problem can be efficiently solved by using Lagrangian relaxation. [ABSTRACT FROM PUBLISHER]

With the fast development of information technology, office automation, quick and effective information document automatic summarization becomes more and more important. Therefore, based on the ICTCLAS segmentation techniques from the CAS Institute of computing technology and Microsoft's Open XML technology, this paper implements an automatic summarization algorithm for Chinese document, and obtains remarkable effect according to the information in Word2007 format documents. Thesis provides a feasible scheme for the automatic abstracting needs in the document management. [ABSTRACT FROM PUBLISHER]