AaKOS: Aspect-adaptive Knowledge-based Opinion Summarization
Guan Wang Weihua Li Edmund M-K. Lai Quan Bai
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.