Extractive Summarization: Limits, Compression, Generalized Model and Heuristics



Document title: Extractive Summarization: Limits, Compression, Generalized Model and Heuristics
Journal: Computación y sistemas
Database: PERIÓDICA
System number: 000423289
ISSN: 1405-5546
Authors: 1
1
Institutions: 1University of Houston, Computer Science Department, Houston, Texas. Estados Unidos de América
Year:
Season: Oct-Dic
Volumen: 21
Number: 4
Country: México
Language: Inglés
Document type: Artículo
Approach: Aplicado, descriptivo
English abstract Due to its promise to alleviate information overload, text summarization has attracted the attention of many researchers. However, it has remained a serious challenge. Here, we first prove empirical limits on the recall (and F1-scores) of extractive summarizers on the DUC datasets under ROUGE evaluation for both the single-document and multi-document summarization tasks. Next we define the concept of compressibility of a document and present a new model of summarization, which generalizes existing models in the literature and integrates several dimensions of the summarization problem, viz., abstractive versus extractive, single versus multi-document, and syntactic versus semantic. Finally, we examine some new and some existing single-document summarization algorithms in a single framework and compare with state of the art summarizers on DUC data
Disciplines: Ciencias de la computación,
Literatura y lingüística
Keyword: Lingüística aplicada,
Procesamiento de lenguaje natural,
Resumen de texto automático,
Integración extractiva,
Heurística
Keyword: Applied linguistics,
Natural language processing,
Automatic summarization,
Extractive summarization,
Heuristics
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