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Incorporating Prior Knowledge into a Transductive Ranking Algorithm for Multi-Document Summarization

Massih-Reza Amini 1 Nicolas Usunier 1
1 MALIRE - Machine Learning and Information Retrieval
LIP6 - Laboratoire d'Informatique de Paris 6
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.
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https://hal.archives-ouvertes.fr/hal-01297940
Contributor : Lip6 Publications <>
Submitted on : Tuesday, April 5, 2016 - 10:52:42 AM
Last modification on : Friday, January 8, 2021 - 5:34:12 PM

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Massih-Reza Amini, Nicolas Usunier. Incorporating Prior Knowledge into a Transductive Ranking Algorithm for Multi-Document Summarization. ACM International Conference on Research and Development in Information Retrieval, Jul 2009, Boston, United States. pp.704-705, ⟨10.1145/1571941.1572087⟩. ⟨hal-01297940⟩

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