Name
Abstract | ||
|---|---|---|
E-commerce is rapidly growing, with review Web sites hosting hundreds of reviews on average for any product. Reading so many reviews is tedious, time-consuming, and with the proposed Gist, unnecessary. We introduce Gist, a system to automatically summarize large amounts of text into informative and actionable key sentences. With unsupervised learning and sentiment analysis, Gist selects the sentences that best characterize a set of reviews. All of this is done in seconds, without prior adjustment or training. Gist extends the current state of the art with a modular system that can take advantage of a priori knowledge and adapt to new domains through easy modification and extension. Gist is a general framework, able to summarize any set of text and easily adapt to specific domains. A robust comparison with state-of-the-art summarization algorithms, on datasets containing hundreds of documents, proves Gist’s ability to effectively summarize text and reviews. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s00500-017-2882-2 | Soft Comput. |
Field | DocType | Volume |
Automatic summarization,Information retrieval,Computer science,Sentiment analysis,A priori and a posteriori,Unsupervised learning,GiST,Modular design | Journal | 23 |
Issue | ISSN | Citations |
5 | 1433-7479 | 1 |
PageRank | References | Authors |
0.35 | 24 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Justin Lovinger | 1 | 2 | 2.04 |
| Iren Valova | 2 | 136 | 25.44 |
| Chad Clough | 3 | 1 | 0.35 |