We discuss a semi-interactive approach to information retrieval which consists of two tasks performed in a sequence. First, the system assists the searcher in building a comprehensive statement of information need, using automatically generated topical summaries of sample documents. Second, the detailed statement of information need is automatically processed by a series of natural language processing routines in order to derive an optimal search query for a statistical information retrieval system. In this paper, we investigate the role of automated document summarization in building effective search statements. We also discuss the results of latest evaluation of our system at the annual Text Retrieval Conference (TREC).
['Tomek Strzalkowski', 'Jin Wang', 'G. Bowden Wise']
Summarization-based Query Expansion in Information Retrieval