1999 Volume 6 Issue 7 Pages 73-92
This paper proposes a new approach to acquiring linguistic knowledge for local context-based summarization. Our summarization method can transform characters, words, and Bunsetsu-phrases to the shorter ones by using linguistic information on some words to be summarized and some words located before and after the summarized words. Our linguistic knowledge for summarization, which is composed of transformation rules and transformation conditions, is automatically acquired from Japanese news corpus. In our corpus, original articles and the human-summarized ones are collected from NHK news text and NHK teletext respectively. The proposed method analyzes original news sentences and the summarized ones by Japanese morphological analyzer, and aligns original words with the summarized words by DP matching based on distances between both of the words. Transformation rules are acquired as the result of the difference. Transformation conditions are extracted as n-gram words located near a transformation rule. We acquired linguistic knowledge from NHK news corpus and obtained a high accuracy rate as a result of a series of experiments to evaluate the linguistic knowledge.
