Personalized and automatic social summarization of events in video

Hannon, John; McCarthy, Kevin; Lynch, James; Smyth, Barry

doi:10.1145/1943403.1943459

Cited by 43 publications

(37 citation statements)

References 7 publications

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“…Several other systems aggregate tweets on a topic into visual summaries, such as TweetMotif [8], the Visual Backchannel [10], TwitInfo [13], and others [11,21,22]. These visualizations must be interpreted by users and do not include sentence-level textual summaries.…”

Section: Related Workmentioning

confidence: 99%

“…As of June 2011, more than 200 million updates were being posted to Twitter each day [1]. Some of these updates describe events that people are participating in or watching through a media source such as television, including natural disasters [25], political debates [21], and sporting events [11]. These updates provide important information about what is happening in real-time, but this information must be summarized or visualized in order to be accessible to human viewers.…”

Section: Introductionmentioning

confidence: 99%

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Summarizing sporting events using twitter

Nichols

Mahmud

Drews

2012

Proceedings of the 2012 ACM International Conference on Intelligent User Interfaces

190

107

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The status updates posted to social networks, such as Twitter and Facebook, contain a myriad of information about what people are doing and watching. During events, such as sports games, many updates are sent describing and expressing opinions about the event. In this paper, we describe an algorithm that generates a journalistic summary of an event using only status updates from Twitter as a source. Temporal cues, such as spikes in the volume of status updates, are used to identify the important moments within an event, and a sentence ranking method is used to extract relevant sentences from the corpus of status updates describing each important moment within an event. We evaluate our algorithm compared to human-generated summaries and the previous best summarization algorithm, and find that the results of our method are superior to the previous algorithm and approach the readability and grammaticality of the human-generated summaries.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Summarizing sporting events using twitter

Nichols

Mahmud

Drews

2012

Proceedings of the 2012 ACM International Conference on Intelligent User Interfaces

190

107

View full text Add to dashboard Cite

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“…News topics are discovered [5] by clustering a large volume of data clustered tweets to discover news topics from the Twitter data. In the domain of sports, Hannon et al [6] produced highlights of the world cup game using poste rate of tweets. These approaches works on offline dataset and detect events several hours after the actual event happened.…”

Section: Related Workmentioning

confidence: 99%

Real Time Event Detection Adopting Incremental TF-IDF based LSH and Event Summary Generation

Kannan¹,

Shanavas²,

Swaminathan³

2018

IJCA

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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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“…However, as also stated in [12], this type of conversational, usergenerated content found in social media, might be used to add value to more traditional event media, such as video. Currently it has not been extensively used for video summarization and the work of Hannon et.…”

Section: Introductionmentioning

confidence: 95%

“…Currently it has not been extensively used for video summarization and the work of Hannon et. al [12] is one of the first to be done towards this direction. In particular timestamped opinions are utilized as the basis for generating video highlights for soccer matches.…”

Section: Introductionmentioning

confidence: 99%

Video abstraction in social media: Augmenting facebook's EdgeRank algorithm in video content presentation

Ntalianis

Doulamis

et al. 2013

2013 IEEE International Conference on Image Processing

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Social networks need to manage and control the drift of huge amounts of information by filtering and summarizing everything, in order to ensure they satisfy users' viewing pleasure. Until now social media content has already been used in a variety of applications such as for ranking of news stories, for profiling of user preferences, even for products' recommendations. However, this type of conversational, user-generated content might be used to add value to more traditional event media, such as video. In this paper we examine the capability of automatically producing meaningful summaries of generic videos. To do so we consider EdgeRank's affinity, weight and time decay parameters and implement a CLARANS-based key-frames extraction scheme. This paper forms an initial study of a social media video abstraction service and experiments indicate its promising performance.

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Personalized and automatic social summarization of events in video

Cited by 43 publications

References 7 publications

Summarizing sporting events using twitter

Summarizing sporting events using twitter

Real Time Event Detection Adopting Incremental TF-IDF based LSH and Event Summary Generation

Video abstraction in social media: Augmenting facebook's EdgeRank algorithm in video content presentation

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