Scoring Sentence Singletons and Pairs for Abstractive Summarization


When writing a summary, humans tend to choose content from one or two sentences and merge them into a single summary sentence. However, the mechanisms behind the selection of one or multiple source sentences remain poorly understood. Sentence fusion assumes multi-sentence input; yet sentence selection methods only work with single sentences and not combinations of them. There is thus a crucial gap between sentence selection and fusion to support summarizing by both compressing single sentences and fusing pairs. This paper attempts to bridge the gap by ranking sentence singletons and pairs together in a unified space. Our proposed framework attempts to model human methodology by selecting either a single sentence or a pair of sentences, then compressing or fusing the sentence(s) to produce a summary sentence. We conduct extensive experiments on both single- and multi-document summarization datasets and report findings on sentence selection and abstraction.

In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Kaiqiang Song
Kaiqiang Song
Senior Research Scientist

Kaiqiang Song (宋凯强) is a Senior Research Scientist at Tencent AI Lab, Seattle, specializing in Natural Language Processing. His research focuses on advancing artificial intelligence through machine learning, NLP, and large language models. He is dedicated to optimizing AI model architectures for practical applications like text summarization and text generation, bridging the gap between foundational AI research and real-world impact.