O pinion D igest: A Simple Framework for Opinion Summarization (2020)

Abstract: We present OpinionDigest, an abstractive opinion summarization framework, which does not rely on gold-standard summaries for training. The framework uses an Aspect-based Sentiment Analysis model to extract opinion phrases from reviews, and trains a Transformer model to reconstruct the original reviews from these extractions. At summarization time, we merge extractions from multiple reviews and select the most popular ones. The selected opinions are used as input to the trained Transformer model, which verbalizes them into an opinion summary. OpinionDigest can also generate customized summaries, tailored to specific user needs, by filtering the selected opinions according to their aspect and/or sentiment. Automatic evaluation on Yelp data shows that our framework outperforms competitive baselines. Human studies on two corpora verify that OpinionDigest produces informative summaries and shows promising customization capabilities.

## introduction the summarization of opinions in customer reviews has received significant attention in the data mining and natural language processing communities. early efforts @xcite focused on producing structured summaries which numerically aggregate the customers' satisfaction about an item across multiple aspects, and often included representative review sentences as evidence. considerable research has recently shifted towards textual opinion summaries, fueled by the increasing success of neural summarization methods @xcite @xcite @xcite . opinion summaries can be extractive, i.e., created by selecting a subset of salient sentences from the input reviews, or abstractive, where summaries are generated from scratch. extractive approaches produce well-formed text, but selecting the sentences which approximate the most popular opinions in the input is challenging. @xcite used sentiment and aspect predictions as a proxy for identifying opinion-rich segments. abstractive methods @xcite , like the one presented in this paper, attempt to model the prevalent opinions in the input and generate text that articulates them. opinion summarization can rarely rely on goldstandard summaries for training (see @xcite for a supervised approach). recent work has utilized end-to-end unsupervised architectures, based on auto-encoders @xcite , where an aggregated representation of the input reviews is fed to a decoder, trained via reconstruction loss to produce reviewlike summaries. similarly to their work, we assume that review-like generation is appropriate for opinion summarization. however, we explicitly deal with opinion popularity, which we believe is crucial for multi-review opinion summarization. additionally, our work is novel in its ability to explicitly control the sentiment and aspects of selected opinions. the aggregation of input reviews is no longer treated as a black box, thus allowing for controllable summarization. specifically, we take a step towards more interpretable and controllable opinion aggregation, as we replace the end-to-end architectures of previous work with a pipeline framework. our method has three components: a) a pre-trained opinion extractor, which identifies opinion phrases in reviews; b) a simple and controllable opinion selector, which merges, ranks, and -optionally-filters the extracted opinions; and c) a generator model, which is trained good location close to the wharf, aquatic park and the many other attraction. loud fridge and ac. good location close to wharf close to aquatic park close to attraction loud fridge loud ac 1. opinion extraction good location close to the wharf, aquatic park and the many other attraction. loud fridge and ac. 2. opinion selection good location close to aquatic park loud fridge great location perfect location near the aquarium loud appliances good location near the aquarium loud appliances multiple original reviews 3. summary generation transformer original review: extracted opinion phrases: opinion clusters: selected opinions: good location, close to fisherman's wharf and other attractions. friendly and nice staff. noisy room with paper thin walls. reconstructed review: good location near the aquarium loud appliances selected opinions: good location, near the aquarium. the appliances were quite loud. generated summary: good location close to wharf close to aquatic park close to attraction loud fridge loud ac extracted opinion phrases: a. training via reconstruction b. summarization to reconstruct reviews from their extracted opinion phrases and can then generate opinion summaries based on the selected opinions. we describe our framework in section 2 and present two types of experiments in section 3: a quantitative comparison against established summarization techniques on the yelp summarization corpus @xcite ; and two user studies, validating the automatic results and our method's ability for controllable summarization. ## opiniondigest framework let d denote a dataset of customer reviews on individual entities {e 1 , e 2 , . . . , e |d| } from a single domain, e.g., restaurants or hotels. for every entity e, we define a review set r @xmath0, where each review is a sequence of words @xmath1, . . . , w n ). within a review, we define a single opinion phrase, @xmath2, . . . w om ), as a subsequence of tokens that expresses the attitude of the reviewer towards a specific aspect of the entity 2 . formally, we define the opinion set of r as o @xmath3, pol i , a i )} |or| @xmath4, where pol i is the sentiment polarity of the i-th phrase (positive, neutral, or negative) and a i is the aspect category it discusses (e.g., a hotel's service, or cleanliness). for each entity e, our task is to abstractively generate a summary s e of the most salient opinions expressed in reviews r e . contrary to previous abstractive methods @xcite , which never explicitly deal with opinion phrases, we put the opinion sets of reviews at the core of our framework, as described in the following sections and illustrated in figure 1 . 2 words that form an opinion may not be contiguous in the review. additionally, a word can be part of multiple opinions. ## conclusion we described opiniondigest, a simple yet powerful framework for abstractive opinion summarization. opiniondigest is a combination of existing absa and seq2seq models and does not require any gold-standard summaries for training. our experiments on the yelp dataset showed that opiniondigest outperforms baseline methods, including a state-of-the-art unsupervised abstractive summarization technique. our user study and qualitative analysis confirmed that our method can generate controllable high-quality summaries, and can summarize large numbers of input reviews.