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MemSum: Extractive Summarization of Long Documents using Multi-step Episodic Markov Decision Processes | | Nianlong Gu
; Elliott Ash
; Richard H.R. Hahnloser
; | | Date: |
19 Jul 2021 | | Abstract: | We introduce MemSum (Multi-step Episodic Markov decision process extractive
SUMmarizer), a reinforcement-learning-based extractive summarizer enriched at
any given time step with information on the current extraction history. Similar
to previous models in this vein, MemSum iteratively selects sentences into the
summary. Our innovation is in considering a broader information set when
summarizing 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
nonetheless obtains state-of-the-art test-set performance (ROUGE score) on long
document datasets (PubMed, arXiv, and GovReport). Supporting analysis
demonstrates that the added awareness of extraction history gives MemSum
robustness against redundancy in the source document. | | Source: | arXiv, 2107.08929 | | Services: | Forum | Review | PDF | Favorites | |
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