Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
11002934 | Computer Speech & Language | 2019 | 30 Pages |
Abstract
Reviews are valuable sources of information for many important decision making tasks. Summarizing the massive amount of reviews, which are available these days on many entities and services, is critical to help users better digest the sentiment about an entity or a service and its aspects (i.e. features of the entity or the service). This article presents a novel aspect-based summarization framework that generates an abstract from multiple reviews of an entity without the need for a handcrafted feature taxonomy or any training data. We generate summaries using Natural Language Generation (NLG) by taking into account the importance of aspects, as well as the association between them. We model these information in the form of a tree, called Aspect Hierarchy Tree (AHT), in which nodes indicate the important aspects and edges indicate the relationship between them. We propose and investigate three alternative content selection and structuring models for the automatic construction of an AHT in our summarization framework: 1) Rhetorical model, which captures the aspects' importance and relationship by looking at the way people discuss and relate the aspects when expressing opinion in their reviews. 2) Conceptual model, which exploits a common-sense knowledge base (e.g. ConceptNet) to find the conceptual association between aspects. 3) Hybrid model, which exploits both the rhetorical and conceptual information. Our abstractive summarization framework has the potential to implement one of the proposed models dependingon the application or apply all three models and let a user choose the output, depending on his/her desire to use the conceptual, rhetorical or both sources of information. Quantitative and qualitative analysis on the resulting AHTs of the three content selection and structuring models for seven entities in three domains shows that the three models generate AHTs that differ in interesting ways in terms of both content (i.e. selected aspects to be included in the summary) and structure (i.e. the relation between aspects).
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Shima Gerani, Giuseppe Carenini, Raymond T. Ng,