Using Aspect Extraction Approaches to Generate Review Summaries and User Profiles
This work addresses the need for better review summarization and user profiling on e-commerce platforms, but it is incremental as it evaluates an existing neural model against baselines.
The paper tackled the problem of extracting aspects from product reviews to generate summaries and user profiles, finding that a simple k-means baseline performed well for sentence extraction and aspect-based profiles effectively captured user preferences in reranking experiments.
Reviews of products or services on Internet marketplace websites contain a rich amount of information. Users often wish to survey reviews or review snippets from the perspective of a certain aspect, which has resulted in a large body of work on aspect identification and extraction from such corpora. In this work, we evaluate a newly-proposed neural model for aspect extraction on two practical tasks. The first is to extract canonical sentences of various aspects from reviews, and is judged by human evaluators against alternatives. A $k$-means baseline does remarkably well in this setting. The second experiment focuses on the suitability of the recovered aspect distributions to represent users by the reviews they have written. Through a set of review reranking experiments, we find that aspect-based profiles can largely capture notions of user preferences, by showing that divergent users generate markedly different review rankings.