Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised Extractive Summarization using Pointwise Mutual Information   Unsupervised approaches to extractive summarization usually rely on a notion
of sentence importance defined by the semantic similarity between a sentence
and the document. We propose new metrics of relevance and redundancy using
pointwise mutual information (PMI) between sentences, which can be easily
computed by a pre-trained language model. Intuitively, a relevant sentence
allows readers to infer the document content (high PMI with the document), and
a redundant sentence can be inferred from the summary (high PMI with the
summary). We then develop a greedy sentence selection algorithm to maximize
relevance and minimize redundancy of extracted sentences. We show that our
method outperforms similarity-based methods on datasets in a range of domains
including news, medical journal articles, and personal anecdotes.

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The categories of this paper are: cond-mat
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The categories of this paper are: solv-int
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The categories of this paper are: hep-lat
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The categories of this paper are: supr-con
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The categories of this paper are: hep-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: funct-an
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The categories of this paper are: alg-geom
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The categories of this paper are: nlin
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The categories of this paper are: chao-dyn
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The categories of this paper are: physics
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The categories of this paper are: quant-ph
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The categories of this paper are: q-bio
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The categories of this paper are: math-ph
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The categories of this paper are: adap-org
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The categories of this paper are: gr-qc