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

被引：15

作者：

Amini, Massih-Reza ^{[1
]}

Usunier, Nicolas ^{[1
]}

机构：

[1] Natl Res Council Canada, Inst Informat Technol, Gatineau, PQ J8X 3X7, Canada

来源：

PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL | 2009年

关键词：

Mutli-document summarization; Learning to Rank;

D O I：

10.1145/1571941.1572087

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：704 / 705

页数：2