文件自動化摘要方法之研究及其在中文文件的應用

Abstract

本論文提出了兩種新的文件摘要方法來摘錄原始文件中的重要語句。第一個方法屬於以文件集為基礎的摘要技術(Corpus-based Approach)，此方法基於統計模型，利用特徵的分析來計算語句重要性。我們提出三個新的想法：1) 利用語句位置重要性的分級以提高不同語句位置的重要性；2)利用詞彙相關程度(Word Co-occurrence)計算找出文件中的新詞，並將新詞加入關鍵詞重要性的計算，以得到更精確的關鍵詞權重特徵值；3) 利用基因演算法訓練計算語句權重的Score Function，以期了解訓練文件集的特性。第二個方法，我們結合潛在語意分析(Latent Semantic Analysis)與主題相關地圖(Text Relationship Map)的概念，用來擷取文件中的概念結構(Conceptual Structure)以期得到語意層面的分析。實驗中，我們收集100篇新台灣週刊中關於政治類的文章，並將上述的兩種方法應用於中文文件的摘要實驗上。效益評估結果顯示，我們所提的方法都有不錯的表現，在壓縮比為30%的情況下，平均來說，召回率分別為52.0%及45.6%。

In this thesis, two novel approaches are proposed to extract important sentences from a document to create its summary. The first is a corpus-based approach using feature analysis. It brings up three new ideas: 1) to employ ranked position to emphasize the significance of sentence position, 2) to reshape word unit to achieve higher accuracy of keyword importance, and 3) to train a score function by the genetic algorithm for obtaining a suitable combination of feature weights. The second approach combines the ideas of latent semantic analysis and text relationship maps to interpret conceptual structures of a document. Both approaches are applied to Chinese text summarization. The two approaches were evaluated by using a data corpus composed of 100 articles about politics from New Taiwan Weekly, and when the compression ratio was 30%, average recalls of 52.0% and 45.6% were achieved respectively.