Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning-Based Single-Document Summarization with Compression and
  Anaphoricity Constraints   We present a discriminative model for single-document summarization that
integrally combines compression and anaphoricity constraints. Our model selects
textual units to include in the summary based on a rich set of sparse features
whose weights are learned on a large corpus. We allow for the deletion of
content within a sentence when that deletion is licensed by compression rules;
in our framework, these are implemented as dependencies between subsentential
units of text. Anaphoricity constraints then improve cross-sentence coherence
by guaranteeing that, for each pronoun included in the summary, the pronoun's
antecedent is included as well or the pronoun is rewritten as a full mention.
When trained end-to-end, our final system outperforms prior work on both ROUGE
as well as on human judgments of linguistic quality.

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The categories of this paper are: hep-ph
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The categories of this paper are: nucl-ex
35
The categories of this paper are: mtrl-th
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The categories of this paper are: cond-mat
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The categories of this paper are: chao-dyn
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The categories of this paper are: eess
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The categories of this paper are: q-bio
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The categories of this paper are: gr-qc
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The categories of this paper are: physics
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The categories of this paper are: acc-phys
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The categories of this paper are: ao-sci
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The categories of this paper are: adap-org
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The categories of this paper are: hep-lat
36
The categories of this paper are: cmp-lg
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The categories of this paper are: plasm-ph
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The categories of this paper are: bayes-an