Hierarchical summarization of large documents

Christopher C. Yang, Fu Lee Wang

Research output: Contribution to journalArticlepeer-review

39 Citations (Scopus)

Abstract

Many automatic text summarization models have been developed in the last decades. Related research in information science has shown that human abstractors extract sentences for summaries based on the hierarchical structure of documents; however, the existing automatic summarization models do not take into account the human abstractor's behavior of sentence extraction and only consider the document as a sequence of sentences during the process of extraction of sentences as a summary. In general, a document exhibits a well-defined hierarchical structure that can be described as fractalsmathematical objects with a high degree of redundancy. In this article, we introduce the fractal summarization model based on the fractal theory. The important information is captured from the source document by exploring the hierarchical structure and salient features of the document. A condensed version of the document that is informatively close to the source document is produced iteratively using the contractive transformation in the fractal theory. The fractal summarization model is the first attempt to apply fractal theory to document summarization. It significantly improves the divergence of information coverage of summary and the precision of summary. User evaluations have been conducted. Results have indicated that fractal summarization is promising and outperforms current summarization techniques that do not consider the hierarchical structure of documents.

Original languageEnglish
Pages (from-to)887-902
Number of pages16
JournalJournal of the American Society for Information Science and Technology
Volume59
Issue number6
DOIs
Publication statusPublished - Apr 2008
Externally publishedYes

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