AaKOS：基于方面自适应知识的观点摘要生成
AaKOS: Aspect-adaptive Knowledge-based Opinion Summarization
AI总结：
针对现有观点摘要方法缺乏方面自适应性、大语言模型难以离线部署的问题，提出面向产品评论的AaKOS模型，可生成聚焦用户感兴趣方面的个性化摘要，性能优于现有最优方法。
摘要：
互联网信息的快速增长使得各类活动、产品及服务相关的观点与评论数量激增，用户在决策时处理所有可用信息既困难又耗时。文本摘要作为自然语言处理（NLP）领域的一项任务，已被广泛研究，旨在通过从长文档或多文档中生成简短且关键的内容，帮助用户快速检索相关信息。以ChatGPT为代表的预训练语言模型的最新进展，展现了大语言模型（LLMs）在文本生成方面的潜力。然而，LLMs需要海量数据与计算资源，难以作为离线应用部署。此外，现有文本摘要方法通常缺乏捕捉观点摘要中多样方面所需的“自适应”特性，这对有特定需求或偏好的用户尤为不利。本文提出了一种面向产品评论的基于方面自适应知识的观点摘要模型AaKOS，可有效捕捉观点摘要所需的自适应特性。该模型针对特定产品的一组评论生成面向特定方面的摘要，能高效为用户提供其感兴趣的特定方面的有用信息，确保生成的摘要更具个性化且信息更丰富。研究人员在真实世界数据集上开展了大量实验以评估所提模型，结果表明，该模型性能优于现有最优（state-of-the-art）方法，在生成聚焦特定方面的摘要时具备自适应性与高效性，能够帮助用户做出明智决策，满足其多样化的兴趣与偏好。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
