{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/abstractive-opinion-tagging","title":"Abstractive Opinion Tagging","arxiv_id":"2101.06880","date":"2021-01-18","proceeding":null,"authors":["Qintong Li","Piji Li","Xinyi Li","Zhaochun Ren","Zhumin Chen","Maarten de Rijke"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2101.06880v2","url_pdf":"https://arxiv.org/pdf/2101.06880v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"abstractive-opinion-tagging","repo_url":"https://github.com/qtli/AOT","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"sentence","task_name":"Sentence"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}