Max Di Marzi, consultant and graph database enthusiast, shares Opinosis: a graph based approach to abstractive summarization of highly redundant opinions, a research project by Kavita Ganesan, ChengXiang Zhai and Jiawei Han at the University of Illinois at Urbana-Champaign.
We present a novel graph-based summarization framework (Opinosis) that generates concise abstractive summaries of highly redundant opinions. Evaluation results on summarizing user reviews show that Opinosis summaries have better agreement with human summaries compared to the baseline extractive method. The summaries are readable, reasonably well-formed and are informative enough to convey the major opinions.Opinions i.e. customer reviews are mainstream in business especially online. Companies get immediate feedback about their products from customers and potentially future sales driven with positive reviews.
Opiniosis takes the free form text people write in reviews, aggregates it, and makes something useful out of it, so I can look at one sentence and not 1000 when looking for details about a review How is this useful? Most companies want to know what their customers are saying about them, but nobody has time to read 1000 responses to that customer survey, so generate a summary instead. Ebay feedback? Twitter posts about a specific hashtag? Text of support e-mails? You get the picture..Read the full article.
Keywords: Customer Reviews • Graph Data • Opiniosis