Aspect-based Review Summary Generation with Diversification

With the popularity of online purchases and user feedback system, huge number of reviews about products, services and social issues enable us to judge items through others' experience. However, generating comprehensive and diversified review summaries usually consumes extensive manual efforts. To solve this problem, we propose a generalized automatic pipeline to generate diversified review summary on different aspects. With the certain review corpus, word embedding matrix can be trained and entities in all aspects will be enlarged accordingly. Combining entities and syntax parsing method, adjectives in every aspect are extracted and divided given their sentiments. The final summary is generated by diverse sampling given aspect and sentiment. This pipeline could be transferred to other review corpora in other languages only if new aspects are specified.