  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.
