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

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. Categories and Subject Descriptors I.2.7 [Artificial Intelligence]: Natural Language Processing—text analysis General Terms Algorithms, Experimentation, Performance Keywords Mutli-document summarization, Learning to Rank

• Type: Conference
• Year: 2009
• Where: SIGIR
• Authors: Massih-Reza Amini, Nicolas Usunier
• Keywords: Extractive Multi-document Summarization, Information Retrieval, Irrelevant Sentences, Ranking Function, SIGIR 2009