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IMPROVING COHERENCE IN MULI-DOCUMENT SUMMARIZATION THROUGH PROPER ORDERING OF SENTENCES

The problem of extracting salient information to include in a summary has been researched extensively in the field of automatic text summarization. However, coherent arrangement of the extracted information has received little attention. Specially, in the case of extractive multi-document text summarization, sentences that convey important information are selected from a set of documents. There is no guarantee that this set of extracted sentences will form a coherent summary by itself. The order of presentation of information is an important factor that affects the coherence of a summary. This thesis focuses on the problem of automatically generating a coherent summary from a given set of documents by ordering the extracted sentences. I propose two different approaches to this problem: a pairwise sentence comparison approach and a bottom-up text structuring approach. The pair-wise sentence comparison approach first compares all possible pairs of sentences and decides partial orderings between the two sentences in pairs. It then creates a total ordering that optimizes a certain function. In the bottom-up text structuring approach, I define four criteria for sentence ordering: chronology, topical-closeness, precedence and succedence. I then use support vector machines to integrate these four different criteria to compute the strength of association between two sentences. For training I use a set of manually ordered summaries. A hierarchical text clustering algorithm is used to produce a total ordering of sentences. I begin by ordering the pair of sentences that has the highest strength of association. I then repeatedly order the two segments of texts with the maximum association strength until a single segment with all sentences ordered is formed. I compare the sentence orderings produced by the proposed algorithm against manually ordered summaries using various rank correlation measures. Moreover, I perform a subjective grading of the generated summaries. Both automatic evaluation and subjective grading suggest that the proposed sentence ordering algorithms significantly outperforms all existing sentence ordering methods for multidocument summarization. Moreover, I investigate the problem of automatically evaluating a sentence ordering for its coherence and propose Average Continuity as an automatic evaluation measure for this task. The proposed automatic evaluation measure reports a high correlation with human ratings.