Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Unsupervised Semantic Sentence Ranking Scheme for Text Documents   This paper presents Semantic SentenceRank (SSR), an unsupervised scheme for
automatically ranking sentences in a single document according to their
relative importance. In particular, SSR extracts essential words and phrases
from a text document, and uses semantic measures to construct, respectively, a
semantic phrase graph over phrases and words, and a semantic sentence graph
over sentences. It applies two variants of article-structure-biased PageRank to
score phrases and words on the first graph and sentences on the second graph.
It then combines these scores to generate the final score for each sentence.
Finally, SSR solves a multi-objective optimization problem for ranking
sentences based on their final scores and topic diversity through semantic
subtopic clustering. An implementation of SSR that runs in quadratic time is
presented, and it outperforms, on the SummBank benchmarks, each individual
judge's ranking and compares favorably with the combined ranking of all judges.

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The categories of this paper are: astro-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: math
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The categories of this paper are: q-alg
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The categories of this paper are: eess
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: q-fin
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The categories of this paper are: math-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: nucl-th
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The categories of this paper are: atom-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: chao-dyn
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The categories of this paper are: cond-mat