Many on-line services allow users to describe their opinions about a product or a service through a review. In order to help other users to find out the major opinion about a given topic, without the effort to read several reviews, multi-document summarisation is required. This research proposes an approach for extractive summarisation, supporting different scoring techniques, such as cosine similarity or divergence, as a method for finding representative sentences. The main contribution of this paper is the definition of an algorithm for sentence removal, developed to maximise the score between the summary and the original document. Instead of ranking the sentences and selecting the most important ones, the algorithm iteratively removes unimportant sentences until a desired compression rate is reached. Experimental results show that variations of the sentence removal algorithm provide good performance.

Automatic Document Summarisation plays a central role in the process of providing the user with a quick access to information. Applications range from the generation of news headlines, to the aggregation of opinions extracted from reviews. Traditional topic-based summarisation systems are not always able to capture the sentiments expressed in a review. Major efforts in sentiment analysis have been put in the tasks of mining and classifying reviews according to their polarity. In this research, we investigate the use of summarisation techniques applied to reviews, and we propose a knowledge-based approach to summarisation, in the context of sentiment analysis. The proposed research is focused on three different aspects. Firstly, we investigate the application of summarisation techniques to sentiment classification. Capturing the key passage of a review can be beneficial for both a sentiment classifier, and for a user who could potentially understand the polarity of a review without reading the full text. Secondly, we investigate how to combine knowledge extracted from the reviews or integrated from external sources, with the purpose of producing opinion-oriented summaries. Thirdly, we analyse the possibility of generating personalised (user-oriented or query-biased) opinion-based summaries.

In on-line reviews, authors often use a short passage to describe the overall feeling about a product or a service. A review as a whole can mention many details not in line with the overall feeling, so capturing this key passage is important to understand the overall sentiment of the review. This paper investigates the use of extractive summarisation in the context of sentiment classification. The aim is to find the summary sentence, or the short passage, which gives the overall sentiment of the review, filtering out potential noisy information. Experiments on a movie review data-set show that subjectivity detection plays a central role in building summaries for sentiment classification. Subjective extracts carry the same polarity of the full text reviews, while statistical and positional approaches are not able to capture this aspect.