Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Specificity-Based Sentence Ordering for Multi-Document Extractive Risk
  Summarization   Risk mining technologies seek to find relevant textual extractions that
capture entity-risk relationships. However, when high volume data sets are
processed, a multitude of relevant extractions can be returned, shifting the
focus to how best to present the results. We provide the details of a risk
mining multi-document extractive summarization system that produces high
quality output by modeling shifts in specificity that are characteristic of
well-formed discourses. In particular, we propose a novel selection algorithm
that alternates between extracts based on human curated or expanded autoencoded
key terms, which exhibit greater specificity or generality as it relates to an
entity-risk relationship. Through this extract ordering, and without the need
for more complex discourse-aware NLP, we induce felicitous shifts in
specificity in the alternating summaries that outperform non-alternating
summaries on automatic ROUGE and BLEU scores, and manual understandability and
preferences evaluations - achieving no statistically significant difference
when compared to human authored summaries.

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The categories of this paper are: hep-th
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The categories of this paper are: hep-lat
14
The categories of this paper are: math-ph
37
The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph
35
The categories of this paper are: mtrl-th
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The categories of this paper are: q-fin
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The categories of this paper are: hep-ex
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The categories of this paper are: q-bio
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The categories of this paper are: dg-ga
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The categories of this paper are: astro-ph
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The categories of this paper are: econ
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The categories of this paper are: hep-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: nlin
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The categories of this paper are: nucl-th