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.

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.

Nowadays, Information Retrieval (IR) systems have to deal with multiple sources of data available in different formats. Datasets can consist of complex and heterogeneous objects with relationships between them. In addition, information needs can vary wildly and they can include different tasks. As a result, the importance of flexibility in IR systems is rapidly growing. This fact is specially important in environments where the information required at different moments is very different and its utility may be contingent on timely implementation. In these cases, how quickly a new problem is solved is as important as how well you solve it.