Top Research Papers on Text Summarization

This paper studies the development of a web application which summarizes the given input text using different models and its deployment and provides a gist of the corpus enabling comprehension in a timely manner.

Text summarization is the process of identifying the most important meaningful information in a document or set of related documents and compressing them into a shorter version preserving its overall meanings.

The role and importance of opinions on public are discussed especially and various techniques that proposed and emerged to discuss about the opinions are mentioned in details.

: In this new era

In the future, it is proposed to propose solution for the limitations have been identified in this paper and to summarize a body of texts by extracting sentences that have particular properties.

The natural language processing research community is developing new methods for summarizing the text mechanically using automatic text summarization system, which produces a summary of the text.

This chapter focuses on showing the effectiveness and shortcomings of text summarization approaches by comparing them and can be categorized based on methodology, function and target reader, dimension, and language.

This paper will discuss the basic concepts of this article by providing the most appropriate definitions, characterization, types and two different methods of automatic text abstraction: extraction and extraction.

This paper gives a review of text summarization and defines the criteria for summary generation, which is among most attractive research areas now-a-days.

Some of the different applications where text summarisation are used are introduced, and a few extractive methodologies have been created for programmed outline age that actualizes various machine learning and enhancement procedures.

Summarization of a text is a procedure of separating or getting the relevant data out of a very large document by using various technologies and methodologies to create a coherent summary including the major points of the original document.

This report will give a brief idea about types of summary, summary evaluation measures and various ways to get summary.

To solve the optimization problem an adaptive differential evolution with novel mutation strategy is employed and the ROUGE value of summaries got by the proposed approach demonstrated its validity, compared to the traditional methods of sentence selection and the top three performing systems for DUC2001 and DUC2002.

This paper proposed various features of Summary Extraction and also analyzed features that are to be applied depending upon the Document.

The base sequence-tosequence model is presented and the latest research that has improved upon it is reviewed, which is the standard method used in machine translations and in sequence-Tosequences prediction, which summarization task is.

Information is accessible in great quantity for every topic on internet assembly the key information in the form of summary would benefit a number of users, so summary can be generated through extractive as well as abstractive methods.

This paper extensively addresses several summarizing strategies depending on intent, volume of data, and outcome.

Hierarchical summarization generates a summary for a document based on the hierarchical structure and salient features of the document and user evaluations indicate that hierarchical summarization outperforms traditional summarization.

The procedure of programmed outline incorporates diminishing a content archive by a program so as to make a synopsis that holds the joke of the first content.

Text Summarization methods can be classified into extractive and abstractive summarization, which are typically based on techniques for sentence extraction and aim to cover the set of sentences that are most important for the overall understanding of a given document.