Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and
  They Are Both Weakly Supervised   We present a neural framework for opinion summarization from online product
reviews which is knowledge-lean and only requires light supervision (e.g., in
the form of product domain labels and user-provided ratings). Our method
combines two weakly supervised components to identify salient opinions and form
extractive summaries from multiple reviews: an aspect extractor trained under a
multi-task objective, and a sentiment predictor based on multiple instance
learning. We introduce an opinion summarization dataset that includes a
training set of product reviews from six diverse domains and human-annotated
development and test sets with gold standard aspect annotations, salience
labels, and opinion summaries. Automatic evaluation shows significant
improvements over baselines, and a large-scale study indicates that our opinion
summaries are preferred by human judges according to multiple criteria.

18
The categories of this paper are: econ
12
The categories of this paper are: nucl-th
11
The categories of this paper are: hep-lat
15
The categories of this paper are: nucl-ex
33
The categories of this paper are: plasm-ph
4
The categories of this paper are: cond-mat
32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
24
The categories of this paper are: dg-ga
8
The categories of this paper are: hep-ex
29
The categories of this paper are: funct-an
20
The categories of this paper are: acc-phys
23
The categories of this paper are: solv-int
10
The categories of this paper are: q-bio
2
The categories of this paper are: math
31
The categories of this paper are: ao-sci