ENTAILMENT-BASED LINEAR SEGMENTATION IN SUMMARIZATION

This paper presents some original methods for text summarization of a single source document by extraction. The methods are based on some of our own text segmentation algorithms. We denote them as logical segmentation because for all these methods (LTT, ArcInt and ArcReal) the score of a sentence is calculated starting from the number of sentences which are entailed by it. For a text (which is a sequence of sentences) the scores form a structure which indicates how the most important sentences alternate with less important ones and organizes the text according to its logical content. The second logical method, Pure Entailment also uses definition of the relation of entailment between two texts. At least to our knowledge, it is for the first time that the relation of Text Entailment between the sentences of a text is used for segmentation and summarization. The third original method applies Dynamic Programming and centering theory to the sentences logically scored as above. The obtained ranked logical segments are used in the summarization. Our methods of segmentation and summarization are applied and evaluated against a manually realized segmentation and summarization of the same text by Donald Richie, "The Koan".