Tweets Summarisation

As we have discussed in Chapter 1, social media has become a rich resource for policy makers and organisations to understand public opinion. However, understanding the sentiment towards different issues and entities as manifested in the large volume of tweets is still a difficult task. The traditional way of collecting such public opinions is by the use of opinion polls, which is costly and the polls themselves carry bias. In recent years we have seen a number of studies linking opinions expressed on Twitter and real world events and stories. For example, an early paper by O’Connor et al [196] found both consumer confidence and presidential approval polls exhibited correlation with Twitter sentiment.

The task of summarising large amount of opinions expressed on Twitter is related to aspect-based summarisation [154, 197, 155], which is concerned with aspects of the target and the sentiment towards each aspect. These methods aim to identify the important features for each aspect and attach relevant reviews or other opinionated sentences to the corresponding feature, providing aspect-based summary in a structured way. The diverse, noisy and unstructured nature of tweets makes its summarisation a more challenging task than summarising product reviews. Louis and Newman [198] presented a concept-based approach that maps business- related tweets into the corresponding concepts learnt using external resources, and selects tweets with the highest average probability of words incorporating sentiment information for each top-ranked cluster. In this thesis, our goal is to construct a

fluent text-based summary for tweets mentioning the same target carrying the same sentiment, and thus different to the structured summary provided by the aspect- based opinion summarisation.

Most work in the literature on tweets summarisation focus on generating summary for real-world events such as natural disasters [199, 200] and sport games [201, 202] or trending topics [203, 24, 204], with the aim to reduce information overload and provide key update for the corresponding story. It has become a pop- ular research task demonstrated by the Microblog [205], Temporal Summarisation [206] and Real-Time Summarisation [207] tracks at Text Retrieval Conferences (TRECs) as well as the more recent Exploitation of Social Media for Emergency Relief and Preparedness (SMERP) track [200] at European Conference on Infor- mation Retrieval (ECIR). Among these works, a majority of early studies pursue either graph-based [208, 203, 209] or term-frequency based [210, 201] approach for extractive summarisation of tweets. A study by Inouye and Kalita [24] compares eight algorithms and reports the simple term-frequency with redundancy reduction based methods, namely multi-post Hybrid tf-idf and SumBasic [211], achieving the closest performance to human evaluation scores, possibly due to the short, unstruc- tured and unconnected nature of tweets. [212] apply summarisation for tackling the topic labelling problem. They also found the frequency based methods outperform- ing the other approaches. [209] present a Pagerank-like algorithm for generating summaries of variable lengths. Time-aware summarisation or timeline generation has also attracted research interest for generating event summary in the form of timeline [202, 213, 214]. Both [202] and [213] rely on tweet burstiness for identifying important moments or sub-events of a sports event. [215] propose a time-aware user behavior model to select representative tweets as summary, based on the user’s history and collaborative social influences from its social circles.

To determine the salience of the tweets, many studies have also focused on incorporating the social influence of users and their social network (e.g. follower-

followee relationship) structure [216, 217, 215, 218]. Finding insightful and informa- tive tweets is challenging, a related work by Swapna and Jiang [219] tackles the task of detecting thoughtful online comments as a classification problem by studying var- ious linguistic features and training a logistic regression model. Some other works use related web contents to provide additional useful topic information to improve summarisation [204, 220]. The motivation of our work in this thesis is related to [25], which also proposes a topic-oriented opinion summarisation framework. How- ever, they use a template-matching method for identifying insightful tweets and the final representative summary tweets are selected through a optimisation procedure, which is different to our approach described in Chapter 6.

While majority of the summarisation research on tweets including all the aforementioned studies choose to adopt the extractive approach, abstractive sum- maries are potentially more cohesive and less redundant. However, there has been few work exploring abstractive summarisation of tweets as it is easily affected by noise or the diversity of tweets. Ganesan et al. [177] introduce a graph-based al- gorithm for merging opinions that share similar textual content and thus reducing redundancy. Because it generates word-graph and explores various sub-paths to construct the final summary, it can still be regarded as a word-level extractive sum- marisation. This method can be used on highly redundant text such as tweets. [221] propose to update the word-graph constantly with tweets which enables for online abstractive summarisation. A more recent work [199] propose a two-stage summarisation framework, which first identifies a set of important tweets using a content-word based extractive approach [222] and then constructs bigram word- graph followed by integer linear programming based optimisation.

In this thesis we investigate and study the feasibility of applying state-of-the- art neural abstractive summarisation for events and opinions expressed on Twitter, with limited training resources. Additionally, we present a visualisation system for displaying opinion summary towards different topics on each day, using the