Comprehensive Review of Opinion Summarization

The abundance of opinions on the web has kindled the study of opinion summarization over the last few years. People have introduced various techniques and paradigms to solving this special task. This survey attempts to systematically investigate the different techniques and approaches used in opinion summarization. We provide a multi-perspective classification of the approaches used and highlight some of the key weaknesses of these approaches. This survey also covers evaluation techniques and data sets used in studying the opinion summarization problem. Finally, we provide insights into some of the challenges that are left to be addressed as this will help set the trend for future research in this area.unpublishednot peer reviewe

A novel concept-level approach for ultra-concise opinion summarization

The Web 2.0 has resulted in a shift as to how users consume and interact with the information, and has introduced a wide range of new textual genres, such as reviews or microblogs, through which users communicate, exchange, and share opinions. The exploitation of all this user-generated content is of great value both for users and companies, in order to assist them in their decision-making processes. Given this context, the analysis and development of automatic methods that can help manage online information in a quicker manner are needed. Therefore, this article proposes and evaluates a novel concept-level approach for ultra-concise opinion abstractive summarization. Our approach is characterized by the integration of syntactic sentence simplification, sentence regeneration and internal concept representation into the summarization process, thus being able to generate abstractive summaries, which is one the most challenging issues for this task. In order to be able to analyze different settings for our approach, the use of the sentence regeneration module was made optional, leading to two different versions of the system (one with sentence regeneration and one without). For testing them, a corpus of 400 English texts, gathered from reviews and tweets belonging to two different domains, was used. Although both versions were shown to be reliable methods for generating this type of summaries, the results obtained indicate that the version without sentence regeneration yielded to better results, improving the results of a number of state-of-the-art systems by 9%, whereas the version with sentence regeneration proved to be more robust to noisy data.This research work has been partially funded by the University of Alicante, Generalitat Valenciana, Spanish Government and the European Commission through the projects, “Tratamiento inteligente de la información para la ayuda a la toma de decisiones” (GRE12-44), “Explotación y tratamiento de la información disponible en Internet para la anotación y generación de textos adaptados al usuario” (GRE13-15), DIIM2.0 (PROMETEOII/2014/001), ATTOS (TIN2012-38536-C03-03), LEGOLANG-UAGE (TIN2012-31224), SAM (FP7-611312), and FIRST (FP7-287607)

Building Contrastive Summaries of Subjective Text Via Opinion Ranking

This article investigates methods to automatically compare entities from opinionated text to help users to obtain important information from a large amount of data, a task known as “contrastive opinion summarization”. The task aims at generating contrastive summaries that highlight differences between entities given opinionated text (written about each entity individually) where opinions have been previously identified. These summaries are made by selecting sentences from the input data. The core of the problem is to find out how to choose these more relevant sentences in an appropriate manner. The proposed method uses a heuristic that makesdecisions according to the opinions found in the input text and to traits that a summary is expected to present. The evaluation is made by measuring three characteristics that contrastive summaries are expected to have: representativity (presence of opinions that are frequent in the input), contrastivity (presence of opinions that highlight differences between entities) and diversity (presence of different opinions to avoid redundancy). The novel method is compared to methods previously published and performs significantly better than them according to the measures used. The main contributions of this work are: a comparative analysis of methods of contrastive opinion summarization, the proposal of a systematic way to evaluate summaries, the development of a new method that performs better than others previously known and the creation of a dataset for the task

BlogSum: A Query-based Summarization Approach to Make Sense of Social Media

With the rapid growth of the Social Web, a large amount of informal opinionated texts are available on numerous topics. However, people can be overwhelmed with this vast amount of information and they need help to find the information of their interests. Natural language tools for automatically analyzing these opinions become necessary to help individuals, organizations, and governments in making timely decisions. To address this need, I proposed a summarization approach for opinionated texts. To validate my approach, BlogSum is developed and evaluated experimentally using current benchmarks. Users can ask BlogSum any question (e.g. Why do people like Chrome better than Firefox?). To answer user's question, BlogSum first retrieves relevant blogs, reviews from the web then generates a concise summary that represents people opinions expressed towards the topic. Since blog summarization is a more recent endeavor, an error analysis was conducted by manually analyzing blog summaries to find there is any information processing difference needed for blogs compared to factual data. This analysis shows that question irrelevance and discourse incoherence, which decrease the overall quality of a summary and reduces the summary coherence, are two major issues for blog summaries. To address question irrelevance and discourse incoherence, in this work a domain-independent schema-based summarization approach is developed that utilizes discourse structures. This approach is based on the automatic identification of discourse relations within candidate sentences in order to instantiate the most appropriate discourse schema and filter and order candidate sentences in the most effective way. BlogSum also needs to deal with opinions, emotions effectively to be successful. BlogSum's overall performance as well as performance for question relevance and coherence was evaluated using various dataset. These results show that the proposed approach can effectively reduce question irrelevance and discourse incoherence and satisfy user's information need