A Novel ILP Framework for Summarizing Content with High Lexical Variety

TL;DR

This paper introduces an ILP-based summarization framework that effectively handles high lexical diversity in content by grouping semantically similar words, outperforming existing baselines across various datasets.

Contribution

The paper presents a novel ILP framework with low-rank approximation to improve summarization of lexically diverse content, addressing a key challenge in extractive summarization.

Findings

Outperforms several extractive baselines

Outperforms neural abstractive systems

Effective across multiple content types

Abstract

Summarizing content contributed by individuals can be challenging, because people make different lexical choices even when describing the same events. However, there remains a significant need to summarize such content. Examples include the student responses to post-class reflective questions, product reviews, and news articles published by different news agencies related to the same events. High lexical diversity of these documents hinders the system's ability to effectively identify salient content and reduce summary redundancy. In this paper, we overcome this issue by introducing an integer linear programming-based summarization framework. It incorporates a low-rank approximation to the sentence-word co-occurrence matrix to intrinsically group semantically-similar lexical items. We conduct extensive experiments on datasets of student responses, product reviews, and news documents. Our approach compares favorably to a number of extractive baselines as well as a neural abstractive summarization system. The paper finally sheds light on when and why the proposed framework is effective at summarizing content with high lexical variety.