%0 Report %1 mitcheltree2018using %A Mitcheltree, Christopher %A Wharton, Veronica %A Saluja, Avneesh %D 2018 %K abae airbnb deeplearning reviews %T Using Aspect Extraction Approaches to Generate Review Summaries and User Profiles %U http://arxiv.org/abs/1804.08666 %X 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.

@techreport{mitcheltree2018using, abstract = {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.}, added-at = {2019-04-11T19:54:10.000+0200}, author = {Mitcheltree, Christopher and Wharton, Veronica and Saluja, Avneesh}, biburl = {https://www.bibsonomy.org/bibtex/2a4b341c016c402094984a20161a40e15/vsathish}, description = {- Use ABAE on airbnb reviews - Attempt to extract canonical sentences from review for summarization - Attempt to create user profiles from their reviews that reveals their preferences for personalization - Work by some undergrads, not super awesome, but may have some interesting ideas}, interhash = {b98c8b5277f438367c6c1078c7fd978b}, intrahash = {a4b341c016c402094984a20161a40e15}, keywords = {abae airbnb deeplearning reviews}, note = {cite arxiv:1804.08666Comment: Equal contribution from first two authors. Accepted for publication in the NAACL 2018 Industry Track}, timestamp = {2019-04-11T19:54:10.000+0200}, title = {Using Aspect Extraction Approaches to Generate Review Summaries and User Profiles}, url = {http://arxiv.org/abs/1804.08666}, year = 2018 }