Open AccessProceedings Article
Event Summarization Using Tweets
Reads0
Chats0
TLDR
It is argued that for some highly structured and recurring events, such as sports, it is better to use more sophisticated techniques to summarize the relevant tweets, and a solution based on learning the underlying hidden state representation of the event via Hidden Markov Models is given.Abstract:
Twitter has become exceedingly popular, with hundreds of millions of tweets being posted every day on a wide variety of topics. This has helped make real-time search applications possible with leading search engines routinely displaying relevant tweets in response to user queries. Recent research has shown that a considerable fraction of these tweets are about "events," and the detection of novel events in the tweet-stream has attracted a lot of research interest. However, very little research has focused on properly displaying this real-time information about events. For instance, the leading search engines simply display all tweets matching the queries in reverse chronological order. In this paper we argue that for some highly structured and recurring events, such as sports, it is better to use more sophisticated techniques to summarize the relevant tweets. We formalize the problem of summarizing event-tweets and give a solution based on learning the underlying hidden state representation of the event via Hidden Markov Models. In addition, through extensive experiments on real-world data we show that our model significantly outperforms some intuitive and competitive baselines.read more
Citations
More filters
Journal ArticleDOI
Data-Driven Techniques in Disaster Information Management
Tao Li,Ning Xie,Chunqiu Zeng,Wubai Zhou,Li Zheng,Yexi Jiang,Yimin Yang,Hsin-Yu Ha,Wei Xue,Yue Huang,Shu-Ching Chen,Jainendra K. Navlakha,S. Sitharama Iyengar +12 more
TL;DR: A general overview of the requirements and system architectures of disaster management systems is presented and state-of-the-art data-driven techniques that have been applied on improving situation awareness as well as in addressing users’ information needs in disaster management are summarized.
BookDOI
A Practical Guide to Sentiment Analysis
TL;DR: The main aim of this book is to provide a feasible research platform to ambitious researchers towards developing the practical solutions that will be indeed beneficial for the authors' society, business and future researches as well.
Proceedings ArticleDOI
Summarizing sporting events using twitter
TL;DR: An algorithm that generates a journalistic summary of an event using only status updates from Twitter as a source is described, and the results are superior to the previous algorithm and approach the readability and grammaticality of the human-generated summaries.
Proceedings ArticleDOI
TwitIE: An Open-Source Information Extraction Pipeline for Microblog Text
TL;DR: This paper introduces each stage of the TwitIE pipeline, which is a modification of the GATE ANNIE open-source pipeline for news text, and an evaluation against some state-of-the-art systems is presented.
Proceedings ArticleDOI
Extracting Situational Information from Microblogs during Disaster Events: a Classification-Summarization Approach
TL;DR: A novel framework which first classifies tweets to extract situational information, and then summarizes the information achieves superior performance compared to state-of-the-art tweet summarization approaches.
References
More filters
Book
Modern Information Retrieval
TL;DR: In this article, the authors present a rigorous and complete textbook for a first course on information retrieval from the computer science (as opposed to a user-centred) perspective, which provides an up-to-date student oriented treatment of the subject.
Proceedings ArticleDOI
Earthquake shakes Twitter users: real-time event detection by social sensors
TL;DR: This paper investigates the real-time interaction of events such as earthquakes in Twitter and proposes an algorithm to monitor tweets and to detect a target event and produces a probabilistic spatiotemporal model for the target event that can find the center and the trajectory of the event location.
Journal ArticleDOI
Bursty and Hierarchical Structure in Streams
TL;DR: The goal of the present work is to develop a formal approach for modeling such “bursts,” in such a way that they can be robustly and efficiently identified, and can provide an organizational framework for analyzing the underlying content.
Proceedings ArticleDOI
Bursty and hierarchical structure in streams
TL;DR: The goal of the present work is to develop a formal approach for modeling such "bursts" in such a way that they can be robustly and efficiently identified, and can provide an organizational framework for analyzing the underlying content.
Proceedings ArticleDOI
Generic text summarization using relevance measure and latent semantic analysis
Yihong Gong,Xin Liu +1 more
TL;DR: This paper proposes two generic text summarization methods that create text summaries by ranking and extracting sentences from the original documents, and uses the latent semantic analysis technique to identify semantically important sentences, for summary creations.