Incorporating Prion Knowledge into a Transductive Ranking Algorithm for Multi-Document Summarization
Massih-Reza Amini(2), Nicolas Usunier(1)
(1) Laboratoire d'Informatique Paris 6
(2) National Research Council Canada
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Kennedy
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boulevard Alexandre Taché
75016 Paris
Gatineau, Canada
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 function over these two sets of sentences bu 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.