Proc. 2023 ACM Int. Conf. on Web Search and Data Mining
(Ed.)
Target-oriented opinion summarization is to profile a target by extracting
user opinions from multiple related documents. Instead
of simply mining opinion ratings on a target (e.g., a restaurant) or
on multiple aspects (e.g., food, service) of a target, it is desirable to
go deeper, to mine opinion on fine-grained sub-aspects (e.g., fish).
However, it is expensive to obtain high-quality annotations at such
fine-grained scale. This leads to our proposal of a new framework,
FineSum, which advances the frontier of opinion analysis in three
aspects: (1) minimal supervision, where no document-summary
pairs are provided, only aspect names and a few aspect/sentiment
keywords are available; (2) fine-grained opinion analysis, where
sentiment analysis drills down to a specific subject or characteristic
within each general aspect; and (3) phrase-based summarization,
where short phrases are taken as basic units for summarization,
and semantically coherent phrases are gathered to improve the
consistency and comprehensiveness of summary. Given a large
corpus with no annotation, FineSum first automatically identifies
potential spans of opinion phrases, and further reduces the noise in
identification results using aspect and sentiment classifiers. It then
constructs multiple fine-grained opinion clusters under each aspect
and sentiment. Each cluster expresses uniform opinions towards
certain sub-aspects (e.g., “fish” in “food” aspect) or characteristics
(e.g., “Mexican” in “food” aspect). To accomplish this, we train a
spherical word embedding space to explicitly represent different
aspects and sentiments. We then distill the knowledge from embedding
to a contextualized phrase classifier, and perform clustering
using the contextualized opinion-aware phrase embedding. Both
automatic evaluations on the benchmark and quantitative human
evaluation validate the effectiveness of our approach.
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