COMPENDIUM: A text summarization system for generating abstracts of research papers
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- 作者：Elena Lloret ; Mar铆a Teresa Rom谩-Ferri ; Manuel Palomar
- 关键词：Human Language Technologies ; NLP applications ; Text summarization ; Information systems
- 刊名：Data and Knowledge Engineering
- 出版年：November, 2013
- 年：2013
- 卷：88
- 期：Complete
- 页码：164-175
- 全文大小：855 K
文摘
This article analyzes the appropriateness of a text summarization system, COMPENDIUM, for generating abstracts of biomedical papers. Two approaches are suggested: an extractive (COMPENDIUME), which only selects and extracts the most relevant sentences of the documents, and an abstractive-oriented one (COMPENDIUME-A), thus facing also the challenge of abstractive summarization. This novel strategy combines extractive information, with some pieces of information of the article that have been previously compressed or fused. Specifically, in this article, we want to study: i) whether COMPENDIUM produces good summaries in the biomedical domain; ii) which summarization approach is more suitable; and iii) the opinion of real users towards automatic summaries. Therefore, two types of evaluation were performed: quantitative and qualitative, for evaluating both the information contained in the summaries, as well as the user satisfaction. Results show that extractive and abstractive-oriented summaries perform similarly as far as the information they contain, so both approaches are able to keep the relevant information of the source documents, but the latter is more appropriate from a human perspective, when a user satisfaction assessment is carried out. This also confirms the suitability of our suggested approach for generating summaries following an abstractive-oriented paradigm.