The document describes an integer linear programming (ILP) model for extracting opinion sentences from user reviews to construct a question database (QDB) for an active opinion acquisition system. The proposed ILP model aims to extract sentences that include opinions, exclude redundant opinions, and have a simple structure, while controlling the number of aspects and sentiments in each extracted sentence. An experiment evaluates the proposed ILP model on a dataset of Japanese product reviews, achieving a precision of 0.88 for opinion sentence extraction to populate the QDB.

1. lWrongly extracted
Background & Goal Proposed method
Related work
Experiments ‒ output examples
lCorrectly extracted
lQGSTEC (Mannem et al., 2010; Ali et al., 2010; Agarwal et al., 2011)
lThe question generation shared task and evaluation challenge
lGiven a text segment, a QGSTEC system is to generate questions whose answers are included in
the input segment
lNeural Question Generation (Zhang et al. 2018)
lA question headline is generated from the original web news article
lAn encoder-decoder model with fully supervised method
lMaximum Coverage Model for opinion sentence extraction (Nishikawa et al.2010)
lThis model adopts an opinion as the concept e_k instead of a word to create a summary that has
as many varieties of opinions as possible.
lDataset: a set of Japanese user review sentences
posted on Rakuten Japan
lWine category 715 sentences
lOriginally created
lResult
l0.88 highest precision
lILP-based sentence extraction
lInteger linear programming formulation
ly represents a result of extraction
ly_i = 1 → i-th sentence is extracted
ly_i = 0 → not extracted
lAOA: Active opinion acquisition system
l Framework to elicit additional opinions form user comments
l Asks a question like s1 after user comment u1
l Requires question DB (QDB)
lTwo-stage strategy for QDB construction
lOpinion sentence extraction & question conversion
lThis work focuses on opinion sentence extraction
Experiments - evaluations
ILP-based Opinion Sentence Extraction from User Reviews
for Question DB Construction
Masakatsu Hamashita and Takashi Inui
University of Tsukuba
Koji Murakami and Keiji Shinzato
Rakuten Institute of Technology
lL_max represents the
maximum output length
l l_i represents the length
of a sentence s_i.
ly^* represents an optimal output according to f(y)
lf(y) measures the quality of an output candidate y
Fundamental model
lExtracted sentences should
satisfy:
1. Include opinion(s)
2. Exclude redundant opinion(s)
3. Have a simple sentence structure
(Nio and Murakami2018)
Additional
constraints
to
control
the
number
of
opinions
in
each
output
sentence
Proposed model
Extension
(1)
Extension (2)
lUnsupervised approach
(s1)(s4) :
no opinions x
(s5):
redundant x
(s3):
complex
structure
x
o
l<a_j , e_k> represents an opinion
la_j: aspect ("aftertaste") in Q_a
le_k: sentiment word ("bitter") in Q_e
lA_max: constant. the maximum number of
aspect in an output sentence
lE_max: constant. the maximum number of
sentiment in an output sentence
lo_ijk: constant. i-th sentence includes
<a_j , e_k> → 1 / not include → 0
lz_jk:
0/1 variable.
<a_j , e_k> is
extracted → 1
/ not extracted → 0
lw_jk: weight of
z_jk
PACLIC34