Assessing sentence scoring techniques for extractive text summarization
Expert Systems with ApplicationsPublished 3 May 2013
Rafael Ferreira, Luciano Cabral, Rafael Dueire Lins, Gabriel Pereira e Silva, Fred Freitas, George D. C. Cavalcanti
Citations315
SJR quartileQ1
SJR score1.85
SNIP2.55
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TL;DR
A quantitative and qualitative assessment of 15 algorithms for sentence scoring available in the literature are described and directions to improve the sentence extraction results obtained are suggested.
Abstract
Text summarization is the process of automatically creating a shorter version of one or more text documents. It is an important way of finding relevant information in large text libraries or in the Internet. Essentially, text summarization techniques are classified as Extractive and Abstractive. Extractive techniques perform text summarization by selecting sentences of documents according to some criteria. Abstractive summaries attempt to improve the coherence among sentences by eliminating redundancies and clarifying the contest of sentences. In terms of extractive summarization, sentence scoring is the technique most used for extractive text summarization. This paper describes and performs a quantitative and qualitative assessment of 15 algorithms for sentence scoring available in the literature. Three different datasets (News, Blogs and Article contexts) were evaluated. In addition, directions to improve the sentence extraction results obtained are suggested.
Keywords
Computer Science
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