Assessing sentence scoring techniques for extractive text summarization |
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Authors: | Rafael Ferreira Luciano de Souza Cabral Rafael Dueire Lins Gabriel Pereira e Silva Fred Freitas George DC Cavalcanti Rinaldo Lima Steven J Simske Luciano Favaro |
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Affiliation: | 1. Informatics Center, Federal University of Pernambuco, Recife, Brazil;2. Hewlett–Packard Labs., Fort Collins, CO 80528, USA;3. Hewlett–Packard Brazil, Barueri, Brazil |
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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. |
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Keywords: | Extractive summarization Sentence scoring methods Summarization evaluation |
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