sekine2003survey A Survey for Multi-Document Summarization

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A Survey for Multi-Document Summarization
Satoshi Sekine and Chikashi Nobata
The Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, Workshop on Text Summarization ; 2003; Edmonton, Canada

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 summarization
B) Sentence Extraction type summarization
C) Axis (type of main topic)
D) Table style summary
In particular, we will describe the last two in detail, as these could lead to a new direction for  multi-summarization research.

1 Introduction
-tantangan: single-doc dan multi-doc summarization performansi tidak jauh dari baseline
-perlu pendekatan baru summarization: mencontoh manusia

-penulis mencoba membuat ringkasan dengan cara: meng highlight frase atau kalimat yang penting, kemudian menghubung-hubungkan sehingga didapat topik2 utama atau yg umum atau berupa list atau tabel. Hasilnya: ringkasan yang bagus
-Meskipun hasil bukan berupa kalimat yang mudah dibaca
-pertanyaan: secara umum berapa jenis "topik utama" yang bis dibuat, dan berapa persen rimgkasn unt tabel.

- tulisan ini ttg manul summary dg 100 document DUC-like

2 Document Sets
3 Task and annotator
4 Free style summarization

5 Sentence Extraction
6 Axis
7 Table
8 Discussion
9 FutureWork