2,211 research outputs found
Prompted Opinion Summarization with GPT-3.5
Large language models have shown impressive performance across a wide variety
of tasks, including text summarization. In this paper, we show that this strong
performance extends to opinion summarization. We explore several pipeline
methods for applying GPT-3.5 to summarize a large collection of user reviews in
a prompted fashion. To handle arbitrarily large numbers of user reviews, we
explore recursive summarization as well as methods for selecting salient
content to summarize through supervised clustering or extraction. On two
datasets, an aspect-oriented summarization dataset of hotel reviews (SPACE) and
a generic summarization dataset of Amazon and Yelp reviews (FewSum), we show
that GPT-3.5 models achieve very strong performance in human evaluation. We
argue that standard evaluation metrics do not reflect this, and introduce three
new metrics targeting faithfulness, factuality, and genericity to contrast
these different methods.Comment: Accepted to ACL (Findings) 202
Efficient Opinion Summarization on Comments with Online-LDA
Customer reviews and comments on web pages are important information n our daily life. For example, we prefer to choose a hotel with positive comments rom previous customers. As the huge amounts of such information demonstrate the haracteristics of big data, it places heavy burdens on the assimilation of the customercontributed pinions. To overcoming this problem, we study an efficient opinion ummarization approach for a set of massive user reviews and comments associated ith an online resource, to summarize the opinions into two categories, i.e., positive nd negative. In this paper, we proposed a framework including: (1) overcoming the ig data problem of online comments using the efficient online-LDA approach; (2) electing meaningful topics from the imbalanced data; (3) summarizing the opinion f comments with high precision and recall. This framework is different from much f the previous work in that the topics are pre-defined and selected the topics for etter opinion summarization. To evaluate the proposed framework, we perform the xperiments on a dataset of hotel reviews for the variety of topics contained. The esults show that our framework can gain a significant performance improvement on pinion summarization
Comprehensive Review of Opinion Summarization
The abundance of opinions on the web has kindled the study of opinion summarization over the last few years. People have introduced various techniques and paradigms to solving this special task. This survey attempts to systematically investigate the different techniques and approaches used in opinion summarization. We provide a multi-perspective classification of the approaches used and highlight some of the key weaknesses of these approaches. This survey also covers evaluation techniques and data sets used in studying the opinion summarization problem. Finally, we provide insights into some of the challenges that are left to be addressed as this will help set the trend for future research in this area.unpublishednot peer reviewe
Using Argument-based Features to Predict and Analyse Review Helpfulness
We study the helpful product reviews identification problem in this paper. We
observe that the evidence-conclusion discourse relations, also known as
arguments, often appear in product reviews, and we hypothesise that some
argument-based features, e.g. the percentage of argumentative sentences, the
evidences-conclusions ratios, are good indicators of helpful reviews. To
validate this hypothesis, we manually annotate arguments in 110 hotel reviews,
and investigate the effectiveness of several combinations of argument-based
features. Experiments suggest that, when being used together with the
argument-based features, the state-of-the-art baseline features can enjoy a
performance boost (in terms of F1) of 11.01\% in average.Comment: 6 pages, EMNLP201
Using Argument-based Features to Predict and Analyse Review Helpfulness
We study the helpful product reviews identification problem in this paper. We
observe that the evidence-conclusion discourse relations, also known as
arguments, often appear in product reviews, and we hypothesise that some
argument-based features, e.g. the percentage of argumentative sentences, the
evidences-conclusions ratios, are good indicators of helpful reviews. To
validate this hypothesis, we manually annotate arguments in 110 hotel reviews,
and investigate the effectiveness of several combinations of argument-based
features. Experiments suggest that, when being used together with the
argument-based features, the state-of-the-art baseline features can enjoy a
performance boost (in terms of F1) of 11.01\% in average.Comment: 6 pages, EMNLP201
AaKOS: Aspect-adaptive Knowledge-based Opinion Summarization
The rapid growth of information on the Internet has led to an overwhelming
amount of opinions and comments on various activities, products, and services.
This makes it difficult and time-consuming for users to process all the
available information when making decisions. Text summarization, a Natural
Language Processing (NLP) task, has been widely explored to help users quickly
retrieve relevant information by generating short and salient content from long
or multiple documents. Recent advances in pre-trained language models, such as
ChatGPT, have demonstrated the potential of Large Language Models (LLMs) in
text generation. However, LLMs require massive amounts of data and resources
and are challenging to implement as offline applications. Furthermore, existing
text summarization approaches often lack the ``adaptive" nature required to
capture diverse aspects in opinion summarization, which is particularly
detrimental to users with specific requirements or preferences. In this paper,
we propose an Aspect-adaptive Knowledge-based Opinion Summarization model for
product reviews, which effectively captures the adaptive nature required for
opinion summarization. The model generates aspect-oriented summaries given a
set of reviews for a particular product, efficiently providing users with
useful information on specific aspects they are interested in, ensuring the
generated summaries are more personalized and informative. Extensive
experiments have been conducted using real-world datasets to evaluate the
proposed model. The results demonstrate that our model outperforms
state-of-the-art approaches and is adaptive and efficient in generating
summaries that focus on particular aspects, enabling users to make
well-informed decisions and catering to their diverse interests and
preferences.Comment: 21 pages, 4 figures, 7 table
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