16 research outputs found
Abstractive Text Summarization by Incorporating Reader Comments
In neural abstractive summarization field, conventional sequence-to-sequence
based models often suffer from summarizing the wrong aspect of the document
with respect to the main aspect. To tackle this problem, we propose the task of
reader-aware abstractive summary generation, which utilizes the reader comments
to help the model produce better summary about the main aspect. Unlike
traditional abstractive summarization task, reader-aware summarization
confronts two main challenges: (1) Comments are informal and noisy; (2) jointly
modeling the news document and the reader comments is challenging. To tackle
the above challenges, we design an adversarial learning model named
reader-aware summary generator (RASG), which consists of four components: (1) a
sequence-to-sequence based summary generator; (2) a reader attention module
capturing the reader focused aspects; (3) a supervisor modeling the semantic
gap between the generated summary and reader focused aspects; (4) a goal
tracker producing the goal for each generation step. The supervisor and the
goal tacker are used to guide the training of our framework in an adversarial
manner. Extensive experiments are conducted on our large-scale real-world text
summarization dataset, and the results show that RASG achieves the
state-of-the-art performance in terms of both automatic metrics and human
evaluations. The experimental results also demonstrate the effectiveness of
each module in our framework. We release our large-scale dataset for further
research.Comment: Accepted by AAAI 201
Product Question Answering in E-Commerce: A Survey
Product question answering (PQA), aiming to automatically provide instant
responses to customer's questions in E-Commerce platforms, has drawn increasing
attention in recent years. Compared with typical QA problems, PQA exhibits
unique challenges such as the subjectivity and reliability of user-generated
contents in E-commerce platforms. Therefore, various problem settings and novel
methods have been proposed to capture these special characteristics. In this
paper, we aim to systematically review existing research efforts on PQA.
Specifically, we categorize PQA studies into four problem settings in terms of
the form of provided answers. We analyze the pros and cons, as well as present
existing datasets and evaluation protocols for each setting. We further
summarize the most significant challenges that characterize PQA from general QA
applications and discuss their corresponding solutions. Finally, we conclude
this paper by providing the prospect on several future directions
Abstractive Opinion Tagging
In e-commerce, opinion tags refer to a ranked list of tags provided by the
e-commerce platform that reflect characteristics of reviews of an item. To
assist consumers to quickly grasp a large number of reviews about an item,
opinion tags are increasingly being applied by e-commerce platforms. Current
mechanisms for generating opinion tags rely on either manual labelling or
heuristic methods, which is time-consuming and ineffective. In this paper, we
propose the abstractive opinion tagging task, where systems have to
automatically generate a ranked list of opinion tags that are based on, but
need not occur in, a given set of user-generated reviews.
The abstractive opinion tagging task comes with three main challenges: (1)
the noisy nature of reviews; (2) the formal nature of opinion tags vs. the
colloquial language usage in reviews; and (3) the need to distinguish between
different items with very similar aspects. To address these challenges, we
propose an abstractive opinion tagging framework, named AOT-Net, to generate a
ranked list of opinion tags given a large number of reviews. First, a
sentence-level salience estimation component estimates each review's salience
score. Next, a review clustering and ranking component ranks reviews in two
steps: first, reviews are grouped into clusters and ranked by cluster size;
then, reviews within each cluster are ranked by their distance to the cluster
center. Finally, given the ranked reviews, a rank-aware opinion tagging
component incorporates an alignment feature and alignment loss to generate a
ranked list of opinion tags. To facilitate the study of this task, we create
and release a large-scale dataset, called eComTag, crawled from real-world
e-commerce websites. Extensive experiments conducted on the eComTag dataset
verify the effectiveness of the proposed AOT-Net in terms of various evaluation
metrics.Comment: Accepted by WSDM 202