Summarizing Procedural Text: Data and Approach

Shen Gao, Haotong Zhang, Xiuying Chen, Rui Yan, Dongyan Zhao


Abstract
Procedural text is a widely used genre that contains many steps of instructions of how to cook a dish or how to conduct a chemical experiment and analyze the procedural text has become a popular task in the NLP field. Since the procedural text can be very long and contains many details, summarizing the whole procedural text or giving an overview for each complicated procedure step can save time for readers and help them to capture the core information in the text. In this paper, we propose the procedural text summarization task with two summarization granularity: step-view and global-view, which summarizes each step in the procedural text separately or gives an overall summary for all steps respectively. To tackle this task, we propose an Entity-State Graph-based Summarizer (ESGS) which is based on state-of-the-art entity state tracking methods and constructs a heterogeneous graph to aggregate contextual information for each procedure. In order to help the summarization model focus on the salient entities, we propose to use the contextualized procedure graph representation to predict the salient entities. Experiments conducted on two datasets verify the effectiveness of our proposed model. Our code and datasets will be released on https://github.com/gsh199449/procedural-summ.
Anthology ID:
2022.findings-emnlp.162
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2022
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2216–2225
Language:
URL:
https://aclanthology.org/2022.findings-emnlp.162
DOI:
10.18653/v1/2022.findings-emnlp.162
Bibkey:
Cite (ACL):
Shen Gao, Haotong Zhang, Xiuying Chen, Rui Yan, and Dongyan Zhao. 2022. Summarizing Procedural Text: Data and Approach. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 2216–2225, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Summarizing Procedural Text: Data and Approach (Gao et al., Findings 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.findings-emnlp.162.pdf