Generating Expert's Review from the Crowds': Integrating a Multi-Attention Mechanism with Encoder-Decoder Framework
Xiaofei Ding, Wenjun Jiang, Jiawei He · 2018
Many consumers resort to others' assessments in product reviews for decision making, while their time is limited to deal with many reviews. Therefore, an expert's review which contains all important features in user-generated reviews, is strongly expected. In this paper, we study "how to generate expert's review from a large number of user-generated reviews (i.e., the crowds)." It can be implemented by text summarization, which mainly has two types of the extractive and the abstractive approaches. However, the former may generate redundant and incoherent summaries, while the latter cannot deal with long sequences. Moreover, both approaches usually neglect the sentiment information. To address the above issues, we propose a novel Expert Review Generation model to integrate a multi-attention mechanism with the encoder-decoder framework. We design a comprehensive preprocess strategy to identify the important sentences while keeping users' sentiment in original reviews, and use them as the input of the encoder-decoder generation model, so as to generate non-redundant and coherent summaries. Experimental results in two real world data sets (Idebate and Rotten Tomatoes) demonstrate that our model performs well in expert review generation.