Incorporating prior knowledge into a transductive ranking algorithm for multi-document summarization

Authors:
Massih R. Amini;Nicolas Usunier
Affiliations:
National Research Council Canada, Gatineau, PQ, Canada;Université Pierre et Marie Curie, Paris, France
Venue:
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Year:
2009

Citing 2
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Abstract

This paper presents a transductive approach to learn ranking functions for extractive multi-document summarization. At the first stage, the proposed approach identifies topic themes within a document collection, which help to identify two sets of relevant and irrelevant sentences to a question. It then iteratively trains a ranking function over these two sets of sentences by optimizing a ranking loss and fitting a prior model built on keywords. The output of the function is used to find further relevant and irrelevant sentences. This process is repeated until a desired stopping criterion is met.