Summarization of Multiple Chinese Technical Articles
Minghui WANG and Hediheko TANAKA
Department of Engineering, The University of Tokyo
Tokyo, JapanAbstract
The automatic summarizer is considered as a kind of software system that canproduce a condensed representation of the content of any document submitted to it. The
research on document summarization recently has been becoming attractive and importantbecause of the explosive increase of the scientific information. One of the major problemsof the existing summarization systems is that it only summarizes a single article at a time.
In this paper, we try to develop a system framework that focuses on performing asummarization of multiple Chinese papers on a specific domain that is considered more
useful for a reader to grasp the outline of a research domain.
Firstly, We will investigate and describe some characteristics of a human expert on
writing a survey for a specific domain. By considering the behavior on writing asummarization for a set of technical papers by a human expert, we can sketch out hiscognitive activity or procedure on this task as in the following. (1) Careful consideration ofthe topic. (2) Retrieval of papers related to the specific domain and corpus construction.
(3)Reading carefully and understanding each of the selected papers. (4)Extraction of
information from papers of the domain. (5) Generating new ideas and viewpoint of his own
after fusing various information together from each paper. (6) Accomplishment of the final
multi-paper summarization based on the above study. Summarization of multiple papers
should be much more difficult than summarizing only single article. Firstly, we should take
into account how to collect the target papers for summarization. Secondly, a multi-paper
summary system should clearly describe the similarity and difference among papers, or it
should be able to extract useful information exactly from papers like human expert does.
In order to conduct study toward multi-paper summarization, we use the corpus
created by the Research Center of Intelligence, Beijing University of Posts andTelecommunications, as the main materials. This corpus consists of a collection of Neural
Network Learning Algorithms articles in Chinese, which were selected from the Chinese
Research Journals and Conference Proceedings in recent years. There are totally 89 papers

on the corpus. The average length of the texts has about 3,000 Chinese characters. Thelongest one had about 6,300 Chinese characters. The shortest one has only about 500
Chinese characters. The corpus was manually marked with some symbols at the head ofthe line such as T, T1, N, U, A1, A2, K1, /P, J etc.. T represents Title, N represents theName of the author, A represents the Abstract of the paper given by the author on thecorpus and so on.
The first task a summarization system needs to perform is that of extracting the mostimportant units in a text. These units can be words, phrasal expressions, clauses, sentence,fragment or paragraphs. . In our multiple text summarization research, we try to utilize
Reference Information Sentences of each paper as the important part to be extracted, which
was firstly applied for multiple English texts summarization by Hietsugu Nanba andManabu Okumura and seems very successful on the task. This is based on the assumptionthat Reference Information Sentences contain some information of the similarity anddifference between the paper and referred papers. Therefore firstly, we browse the text
from the beginning to the end to look for the referred position, and extract fragments of thepaper where the author describes the essence of referred paper and the difference with hispaper. Then with the information of reference areas, we can generate multiple papers
summarization through categorizing the types of reference relationship. At present, there
are three Reference Type classifications. (1) The Similarity-type reference means that thecitation is to base on other researchers’ theories. (2) The Difference-type reference
represents that the reference to compare with related work or to point out their problems.
(3) The Other-type reference refers to the reference other than the Similarity-type and theDifference-type. Considering the linguistic characteristics of the Chinese text, there arealso some cue words to indicate such a connection between sentences beside the obvious
citation information. We have selected some cue words to help the extraction for thereference area.
In this paper, we proposed a prototype system framework to perform thesummarization of multi-paper on the technical domain by extracting the reference
information of each paper in the corpus. This work is still at early stage. The whole system
hasn’t been completely implemented yet and it is also difficult to develop a tool to evaluate
the method reasonable or workable at present. However, This seed work will allow us toconduct further study toward automatic multi-paper summarization.