Sentiment diversification for short review summarization

Mohammed Al-Dhelaan, A. Al-Suhaim
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引用次数: 4

Abstract

With the abundance of reviews published on the Web about a given product, consumers are looking for ways to view major opinions that can be presented in a quick and succinct way. Reviews contain many different opinions, making the ability to show a diversified review summary that focus on coverage and diversity a major goal. Most review summarization work focuses on showing salient reviews as a summary which might ignore diversity in summaries. In this paper, we present a graph-based algorithm that is capable of producing extractive summaries that are both diversified from a sentiment point of view and topically well-covered. First, we use statistical measures to find topical words. Then we split the dataset based on the sentiment class of the reviews and perform the ranking on each sentiment graph. When compared with different baselines, our approach scores best in most ROUGE metrics. Specifically, our approach shows improvements of 3.9% in ROUGE-1 and 1.8% in ROUGE-L in comparison with the best competing baseline.
情绪多元化短评总结
随着网络上发布的关于某一产品的大量评论,消费者正在寻找能够以快速、简洁的方式显示主要意见的方法。评审包含了许多不同的意见,这使得能够显示一个专注于覆盖范围和多样性的多样化评审总结成为一个主要目标。大多数评论总结工作都侧重于将突出的评论作为摘要显示,这可能会忽略摘要的多样性。在本文中,我们提出了一种基于图的算法,该算法能够生成从情感角度多样化且主题覆盖良好的摘录摘要。首先,我们使用统计方法来寻找热门词汇。然后根据评论的情感类对数据集进行拆分,并对每个情感图进行排序。当与不同的基线进行比较时,我们的方法在大多数ROUGE指标中得分最高。具体来说,我们的方法显示,与最佳竞争基线相比,ROUGE-1和ROUGE-L的改进分别为3.9%和1.8%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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