Assessing sentence scoring techniques for extractive text summarization

被引：195

作者：

Ferreira, Rafael ^{[1
]}

Cabral, Luciano de Souza ^{[1
]}

Lins, Rafael Dueire ^{[1
]}

Pereira e Silva, Gabriel ^{[1
]}

Freitas, Fred ^{[1
]}

Cavalcanti, George D. C. ^{[1
]}

Lima, Rinaldo ^{[1
]}

Simske, Steven J. ^{[2
]}

Favaro, Luciano ^{[3
]}

机构：

[1] Univ Fed Pernambuco, Informat Ctr, Recife, PE, Brazil

[2] Hewlett Packard Labs, Ft Collins, CO 80528 USA

[3] Hewlett Packard Brazil, Barueri, Brazil

来源：

EXPERT SYSTEMS WITH APPLICATIONS | 2013年 / 40卷 / 14期

关键词：

Extractive summarization; Sentence scoring methods; Summarization evaluation;

D O I：

10.1016/j.eswa.2013.04.023

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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. (C) 2013 Elsevier Ltd. All rights reserved.

引用

页码：5755 / 5764

页数：10

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