A Sentence Compression Based Framework to Query-Focused Multi-Document Summarization

TL;DR

This paper introduces a novel sentence compression framework for query-focused multi-document summarization, leveraging learning-based models and an innovative beam search to improve summary relevance and conciseness.

Contribution

It presents a new compression-based approach with a scoring function that integrates linguistic and query relevance metrics, outperforming state-of-the-art systems.

Findings

Achieved 8.0% and 5.4% improvements in ROUGE-2 scores on DUC datasets.

Developed a beam search decoder for efficient high-probability compressions.

Integrated linguistic motivation and query relevance into compression scoring.

Abstract

We consider the problem of using sentence compression techniques to facilitate query-focused multi-document summarization. We present a sentence-compression-based framework for the task, and design a series of learning-based compression models built on parse trees. An innovative beam search decoder is proposed to efficiently find highly probable compressions. Under this framework, we show how to integrate various indicative metrics such as linguistic motivation and query relevance into the compression process by deriving a novel formulation of a compression scoring function. Our best model achieves statistically significant improvement over the state-of-the-art systems on several metrics (e.g. 8.0% and 5.4% improvements in ROUGE-2 respectively) for the DUC 2006 and 2007 summarization task.