PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Incorporating Prior Knowledge into a Transductive Ranking Algorithm for Multi-Document Summarization
Massih Amini and Nicolas Usunier
In: SIGIR 2009, 19-23 Jul 2009, Boston, MA, USA.


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.

EPrint Type:Conference or Workshop Item (Poster)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
Information Retrieval & Textual Information Access
ID Code:6427
Deposited By:Nicolas Usunier
Deposited On:08 March 2010