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Recently, twitter users are leveraged to detect social and physical events such as festivals and traffic jam at real time. Real time event detection and summarization from Cricket sports is the process of detecting events such as boundary at real time from live Cricket tweet stream as soon as event happens and generating a quick game summary. This is an interesting, yet a complex problem. Because of the need for rapid detection of sports events and for the generation of a concise summary from huge volume of tweets for Cricket enthusiasts. In this paper, a novel framework is proposed for detecting key events from live Cricket tweets and for generating a game summary using the crawled tweets. Feature vectors of live tweets are created using incremental TF-IDF representation and tweet clusters are discovered using Locality Sensitive Hashing (LSH) where the post rate of each cluster determines the key event. A key event is recognized from that cluster using our domain specific event lexicon. Then, important moments from the crawled tweets are computed by identifying the spikes in the tweets volume. Topk tweets from each moment are selected by ranking tweets on top-k words. Representative tweets from top-k tweets are identified using Jaccard similarity. The evaluation on 2017 IPL T20 Cricket live tweets using ROC measure shows that the proposed incremental TF-IDF based LSH approach detects key events with nearly 95% true positive rate and around 5% false positive rate. The proposed game summarization algorithm generates summaries which are readable and competitive to human tailored summaries.

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Abstract. Video summarization aims at producing a compact version of a full-length video while preserving the significant content of the original video. Movie summarization condenses a full-length movie into a summary that still retains the most significant and interesting content of the original movie. In the past, several movie summarization systems have been proposed to generate a movie summary based on low-level video features such as color, motion, texture, etc. However, a generic summary, which is common to everyone and is produced based only on low-level video features will not satisfy every user. As users' preferences for the summary differ vastly for the same movie, there is a need for a personalized movie summarization system nowadays. To address this demand, this paper proposes a novel system to generate semantically meaningful video summaries for the same movie, which are tailored to the preferences and interests of a user. For a given movie, shots and scenes are automatically detected and their high-level features are semi-automatically annotated. Preferences over high-level movie features are explicitly collected from the user using a query interface. The user preferences are generated by means of a stored-query. Movie summaries are generated at shot level and scene level, where shots or scenes are selected for summary skim based on the similarity measured between shots and scenes, and the user's preferences. The proposed movie summarization system is evaluated subjectively using a sample of 20 subjects with eight movies in the English language. The quality of the generated summaries is assessed by informativeness, enjoyability, relevance, and acceptance metrics and Quality of Perception measures. Further, the usability of the proposed summarization system is subjectively evaluated by conducting a questionnaire survey. The experimental results on the performance of the proposed movie summarization approach show the potential of the proposed system.