A survey for multi-document summarization

Authors:
Satoshi Sekine;Chikashi Nobata
Affiliations:
New York University, New York, NY;Communications Reserach Laboratory, Seika-chou, Soraku-gun Kyoto, Japan
Venue:
HLT-NAACL-DUC '03 Proceedings of the HLT-NAACL 03 on Text summarization workshop - Volume 5
Year:
2003

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Abstract

Automatic Multi-Document summarization is still hard to realize. Under such circumstances, we believe, it is important to observe how humans are doing the same task, and look around for different strategies.We prepared 100 document sets similar to the ones used in the DUC multi-document summarization task. For each document set, several people prepared the following data and we conducted a survey.A) Free style summarizationB) Sentence Extraction type summarizationC) Axis (type of main topic)D) Table style summaryIn particular, we will describe the last two in detail, as these could lead to a new direction for multi-summarization research.