Unsupervised Summarization with Customized Granularities

• URL: http://arxiv.org/abs/2201.12502v1
• Date: Sat, 29 Jan 2022 05:56:35 GMT
• Title: Unsupervised Summarization with Customized Granularities
• Authors: Ming Zhong, Yang Liu, Suyu Ge, Yuning Mao, Yizhu Jiao, Xingxing Zhang, Yichong Xu, Chenguang Zhu, Michael Zeng, Jiawei Han
• Abstract summary: We propose the first unsupervised multi-granularity summarization framework, GranuSum. By inputting different numbers of events, GranuSum is capable of producing multi-granular summaries in an unsupervised manner.
• Score: 76.26899748972423
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text summarization is a personalized and customized task, i.e., for one document, users often have different preferences for the summary. As a key aspect of customization in summarization, granularity is used to measure the semantic coverage between summary and source document. Coarse-grained summaries can only contain the most central event in the original text, while fine-grained summaries cover more sub-events and corresponding details. However, previous studies mostly develop systems in the single-granularity scenario. And models that can generate summaries with customizable semantic coverage still remain an under-explored topic. In this paper, we propose the first unsupervised multi-granularity summarization framework, GranuSum. We take events as the basic semantic units of the source documents and propose to rank these events by their salience. We also develop a model to summarize input documents with given events as anchors and hints. By inputting different numbers of events, GranuSum is capable of producing multi-granular summaries in an unsupervised manner. Meanwhile, to evaluate multi-granularity summarization models, we annotate a new benchmark GranuDUC, in which we write multiple summaries of different granularities for each document cluster. Experimental results confirm the substantial superiority of GranuSum on multi-granularity summarization over several baseline systems. Furthermore, by experimenting on conventional unsupervised abstractive summarization tasks, we find that GranuSum, by exploiting the event information, can also achieve new state-of-the-art results under this scenario, outperforming strong baselines.

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