Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6855430 | Expert Systems with Applications | 2016 | 19 Pages |
Abstract
The volume of text data has been growing exponentially in the last years, mainly due to the Internet. Automatic Text Summarization has emerged as an alternative to help users find relevant information in the content of one or more documents. This paper presents a comparative analysis of eighteen shallow sentence scoring techniques to compute the importance of a sentence in the context of extractive single- and multi-document summarization. Several experiments were made to assess the performance of such techniques individually and applying different combination strategies. The most traditional benchmark on the news domain demonstrates the feasibility of combining such techniques, in most cases outperforming the results obtained by isolated techniques. Combinations that perform competitively with the state-of-the-art systems were found.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Hilário Oliveira, Rafael Ferreira, Rinaldo Lima, Rafael Dueire Lins, Fred Freitas, Marcelo Riss, Steven J. Simske,