Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Summarizing Reviews with Variable-length Syntactic Patterns and Topic
  Models   We present a novel summarization framework for reviews of products and
services by selecting informative and concise text segments from the reviews.
Our method consists of two major steps. First, we identify five frequently
occurring variable-length syntactic patterns and use them to extract candidate
segments. Then we use the output of a joint generative sentiment topic model to
filter out the non-informative segments. We verify the proposed method with
quantitative and qualitative experiments. In a quantitative study, our approach
outperforms previous methods in producing informative segments and summaries
that capture aspects of products and services as expressed in the
user-generated pros and cons lists. Our user study with ninety users resonates
with this result: individual segments extracted and filtered by our method are
rated as more useful by users compared to previous approaches by users.

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The categories of this paper are: q-alg
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The categories of this paper are: ao-sci
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The categories of this paper are: hep-ph
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The categories of this paper are: q-fin
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The categories of this paper are: bayes-an
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The categories of this paper are: gr-qc
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The categories of this paper are: chao-dyn
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The categories of this paper are: patt-sol
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The categories of this paper are: hep-lat
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The categories of this paper are: nucl-th
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The categories of this paper are: econ
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The categories of this paper are: stat
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The categories of this paper are: dg-ga
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The categories of this paper are: math-ph
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The categories of this paper are: solv-int
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The categories of this paper are: q-bio