{"id":238310,"date":"2016-02-12T00:00:00","date_gmt":"2016-02-12T08:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/microsummarization-of-online-reviews-an-experimental-study\/"},"modified":"2018-10-16T20:06:05","modified_gmt":"2018-10-17T03:06:05","slug":"microsummarization-of-online-reviews-an-experimental-study","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/microsummarization-of-online-reviews-an-experimental-study\/","title":{"rendered":"Microsummarization of Online Reviews: An Experimental Study"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Mobile and location-based social media applications provide platforms for users to share brief opinions about products, venues, and services. These quickly typed opinions, or <em>microreviews<\/em>, are a valuable source of current sentiment on a wide variety of subjects. However, there is currently little research on how to mine this information to present it back to users in easily consumable way. In this paper, we introduce the task of <em>microsummarization<\/em>, which combines sentiment analysis, summarization, and entity recognition in order to surface key content to users. We explore unsupervised and supervised methods for this task, and find we can reliably extract relevant entities and the sentiment targeted towards them using crowdsourced labels as supervision. In an end-to-end evaluation, we find our best-performing system is vastly preferred by judges over a traditional extractive summarization approach. This work motivates an entirely new approach to summarization, incorporating both sentiment analysis and item extraction for modernized, at-a-glance presentation of public opinion.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Mobile and location-based social media applications provide platforms for users to share brief opinions about products, venues, and services. These quickly typed opinions, or microreviews, are a valuable source of current sentiment on a wide variety of subjects. However, there is currently little research on how to mine this information to present it back to [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13556,13545],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-238310","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-research-area-human-language-technologies","msr-locale-en_us"],"msr_publishername":"AAAI - Association for the Advancement of Artificial 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Editing","post_name":"intelligent-editing","post_type":"msr-project","post_date":"2016-09-23 04:05:10","post_modified":"2017-06-16 10:32:52","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/intelligent-editing\/","post_excerpt":"The Intelligent Editing Project seeks to apply neural networks and other modern machine learning techniques to furnish editorial assistance. \u00a0We look beyond traditional grammatical error checking to focus on facilitating writers by providing them with fluent, meaningful text editing\u00a0support that is appropriate to their objectives and their targeted readership. \u00a0Our interests include sentence compression and summarization,\u00a0 paraphrasing and stylistic variation, and writing assistance for non-native writers.\u00a0\u00a0 \u00a0The MSR Abstractive Text Compression Dataset described in 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