8,428 research outputs found
Automaic Text Summarization
Automatic summarization is the procedure of decreasing the content of a document with a machine (computer) program so as to make a summary that holds the most critical sentences of the text file (document). Extracting summary from the document is a difficult task for human beings. Therefore to generate summary automatically has to facilitate several challenges; as the system automates it can only extract the required information from the original document. As the issue of information overload has grown - trouble has been initiated, and as the measure of data has extended, so has eagerness to customize it. It is uncommonly troublesome for individuals to physically condense broad reports of substance. Automatic Summarization systems may be classified into extractive and abstractive summary. An extractive summary method involves selecting indispensable sentences from the record and interfacing them into shorter structure. The vitality of sentences chosen is focused around factual and semantic characteristics of sentences. Extractive method work by selecting a subset of existing words, or sentences in the text file (content document) to produce the summary of input text file. The looking of important data from a huge content document is exceptionally difficult occupation for the user consequently to programmed concentrate the imperative information or summary of the content record. This summary helps the users to reduce time instead Of reading the whole text document and it provide quick knowledge from the large text file. The extractive summarization are commonly focused around techniques for sentence extraction to blanket the set of sentences that are most important for the general understanding of a given text file. In frequency based technique, obtained summary makes more meaning. But in k-means clustering due to out of order extraction, summary might not make sens
Automatic text summarization
Automatic summarization is the procedure of decreasing the content of a document with a machine (computer) program so as to make a summary that holds the most critical sentences of the text file (document). Extracting summary from the document is a difficult task for human beings. Therefore to generate summary automatically has to facilitate several challenges; as the system automates it can only extract the required information from the original document. As the issue of information overload has grown - trouble has been initiated, and as the measure of data has extended, so has eagerness to customize it. It is uncommonly troublesome for individuals to physically condense broad reports of substance. Automatic Summarization systems may be classified into extractive and abstractive summary. An extractive summary method involves selecting indispensable sentences from the record and interfacing them into shorter structure. The vitality of sentences chosen is focused around factual and semantic characteristics of sentences. Extractive method work by selecting a subset of existing words, or sentences in the text file (content document) to produce the summary of input text file. The looking of important data from a huge content document is exceptionally difficult occupation for the user consequently to programmed concentrate the imperative information or summary of the content record. This summary helps the users to reduce time instead Of reading the whole text document and it provide quick knowledge from the large text file. The extractive summarization are commonly focused around techniques for sentence extraction to blanket the set of sentences that are most important for the general understanding of a given text file. In frequency based technique, obtained summary makes more meaning. But in k-means clustering due to out of order extraction, summary might not make sense
Text Summarization Techniques: A Brief Survey
In recent years, there has been a explosion in the amount of text data from a
variety of sources. This volume of text is an invaluable source of information
and knowledge which needs to be effectively summarized to be useful. In this
review, the main approaches to automatic text summarization are described. We
review the different processes for summarization and describe the effectiveness
and shortcomings of the different methods.Comment: Some of references format have update
Abstractive Multi-Document Summarization via Phrase Selection and Merging
We propose an abstraction-based multi-document summarization framework that
can construct new sentences by exploring more fine-grained syntactic units than
sentences, namely, noun/verb phrases. Different from existing abstraction-based
approaches, our method first constructs a pool of concepts and facts
represented by phrases from the input documents. Then new sentences are
generated by selecting and merging informative phrases to maximize the salience
of phrases and meanwhile satisfy the sentence construction constraints. We
employ integer linear optimization for conducting phrase selection and merging
simultaneously in order to achieve the global optimal solution for a summary.
Experimental results on the benchmark data set TAC 2011 show that our framework
outperforms the state-of-the-art models under automated pyramid evaluation
metric, and achieves reasonably well results on manual linguistic quality
evaluation.Comment: 11 pages, 1 figure, accepted as a full paper at ACL 201
Generating indicative-informative summaries with SumUM
We present and evaluate SumUM, a text summarization system that takes a raw technical text as input and produces an indicative informative summary. The indicative part of the summary identifies the topics of the document, and the informative part elaborates on some of these topics according to the reader's interest. SumUM motivates the topics, describes entities, and defines concepts. It is a first step for exploring the issue of dynamic summarization. This is accomplished through a process of shallow syntactic and semantic analysis, concept identification, and text regeneration. Our method was developed through the study of a corpus of abstracts written by professional abstractors. Relying on human judgment, we have evaluated indicativeness, informativeness, and text acceptability of the automatic summaries. The results thus far indicate good performance when compared with other summarization technologies
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