A Novel ILP Framework for Summarizing Content with High Lexical Variety
This addresses the problem of summarizing diverse user-generated content for applications like education and e-commerce, but it is incremental as it builds on existing ILP and low-rank approximation methods.
The paper tackles the challenge of summarizing content with high lexical variety, such as student responses and product reviews, by introducing an integer linear programming-based framework that groups semantically-similar lexical items, and it shows favorable performance compared to extractive and abstractive baselines in experiments.
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.