MemSum: Extractive Summarization of Long Documents Using Multi-Step Episodic Markov Decision Processes
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Date
2022-05Type
- Conference Paper
ETH Bibliography
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Abstract
We introduce MemSum (Multi-step Episodic Markov decision process extractive SUMmarizer), a reinforcement-learning-based extractive summarizer enriched at each step with information on the current extraction history. When MemSum iteratively selects sentences into the summary, it considers a broad information set that would intuitively also be used by humans in this task: 1) the text content of the sentence, 2) the global text context of the rest of the document, and 3) the extraction history consisting of the set of sentences that have already been extracted. With a lightweight architecture, MemSum obtains state-of-the-art test-set performance (ROUGE) in summarizing long documents taken from PubMed, arXiv, and GovReport. Ablation studies demonstrate the importance of local, global, and history information. A human evaluation confirms the high quality and low redundancy of the generated summaries, stemming from MemSum's awareness of extraction history. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000570882Publication status
publishedExternal links
Book title
Proceedings of the 60th Annual Meeting of the Association for Computational LinguisticsVolume
Pages / Article No.
Publisher
Association for Computational LinguisticsEvent
Organisational unit
03774 - Hahnloser, Richard H.R. / Hahnloser, Richard H.R.
Funding
182638 - The roles of vocal communication in pair formation and cultural learning in songbirds (SNF)
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ETH Bibliography
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