@inproceedings{zhong2022unsupervised,
author = {Zhong, Ming and Liu, Yang and Mao, Yuning and Xu, Yichong and Ge, Suyu and Jiao, Yizhu and Zhang, Xingxing and Zhu, Chenguang and Zeng, Michael and Han, Jiawei},
title = {Unsupervised Summarization with Customized Granularities},
booktitle = {Findings of Empirical Methods in Natural Language Processing (EMNLP), Abu Dhabi, the United Arab Emirates, 2022.},
year = {2022},
month = {December},
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.},
url = {http://approjects.co.za/?big=en-us/research/publication/unsupervised-summarization-with-customized-granularities/},
}