Summarizing Similarities and Differences Among Related Documents

In many modern information retrieval applications, a common problem which arises is the existence of multiple documents covering similar information, as in the case of multiple news stories about an event or a sequence of events. A particular challenge for text summarization is to be able to summarize the similarities and differences in information content among these documents. The approach described here exploits the results of recent progress in information extraction to represent salient units of text and their relationships. By exploiting meaningful relations between units based on an analysis of text cohesion and the context in which the comparison is desired, the summarizer can pinpoint similarities and differences, and align text segments. In evaluation experiments, these techniques for exploiting cohesion relations result in summaries which (i) help users more quickly complete a retrieval task (ii) result in improved alignment accuracy over baselines, and (iii) improve identification of topic-relevant similarities and differences.