Post Summarization of Microblogs of Sporting Events

2018

Immense volume of short messages that is tweets are being shared among various clients and information on long range informal communication locales and microblogging destinations, for example, Twitter, Facebook and so forth. Twitter gets more than 400 million tweets for every day. Constant examination is extremely troublesome and testing undertaking on such gigantic information likewise questioning and recovery of information is additionally troublesome. Such a huge number of tweets contain colossal measure of commotion and repetition. Existing frameworks were generally chipped away at the static and the constrained information. The different existing frameworks were proposed to address these issues and furthermore they gave some arrangement. Summarization is the way toward involving a content document in such way that short summary produced by using the essential keywords of the first document. There is need of dynamic way to deal with condense information delivered by Twitter feeds. This paper proposes the novel method, which produce the significant substance based summery inside less measure of time. Especially, in the proposed framework multisubject summarization is performed on the online dataset which thusly require the less measure of time when contrasted with the other existing framework. So time productivity is improved by using the proposed framework.

2012

Social media have drastically altered the concept of information contribution and dissemination by empowering the general public to publish and distribute user-generated content. These social media contributions may be viewed as expressions of humans acting as sensors, reporting events and activities in which they participate, or commenting on others that are somehow affecting them, or catching their attention. Thematically, the content of such media is diverse, ranging from reporting events like an earthquake to making mundane comments, pop culture references, or daily activity reports. Regardless of the topic, this information always has a temporal component, in the form of its submission time. Social media feeds also often have geolocation information associated with them, available in the form of precise coordinates, or as location descriptions listing for example only a city name. In this paper we focus on the spatiotemporal content of twitter feeds in order to assess their use as a hybrid form of a sensor network to monitor evolving events. Our objective is to investigate how social media contributions can be utilized to study the spatiotemporal evolution of dynamic sociocultural events. Towards this goal we use as a representative case the events of the Occupy Wall Street (OWS) movement in New York City, NY, on the International Day of Action of November 17th, 2011. We use twitter as a representative example to harvest social media for our study. We collected geolocated tweets during that day, making reference to the Occupy Wall Street movement (e.g. through its associated hashtags and usernames, such as #ows) and analyze them to investigate how well they capture that day’s activities.

HT'12 - Proceedings of 23rd ACM Conference on Hypertext and Social Media, 2012

This paper explores the real-time summarization of scheduled events such as soccer games from torrential flows of Twitter streams. We propose and evaluate an approach that substantially shrinks the stream of tweets in real-time, and consists of two steps: (i) sub-event detection, which determines if something new has occurred, and (ii) tweet selection, which picks a representative tweet to describe each sub-event. We compare the summaries generated in three languages for all the soccer games in Copa America 2011 to reference live reports offered by Yahoo! Sports journalists. We show that simple text analysis methods which do not involve external knowledge lead to summaries that cover 84% of the sub-events on average, and 100% of key types of sub-events (such as goals in soccer). Our approach should be straightforwardly applicable to other kinds of scheduled events such as other sports, award ceremonies, keynote talks, TV shows, etc.

Detecting events using social media such as Twitter has many useful applications in real-life situations. Many algorithms which all use different information sources—either textual, temporal, geographic or community features—have been developed to achieve this task. Semantic information is often added at the end of the event detection to classify events into semantic topics. But semantic information can also be used to drive the actual event detection, which is less covered by academic research. We therefore supplemented an existing baseline event clustering algorithm with semantic information about the tweets in order to im- prove its performance. This paper lays out the details of the semantics-driven event clustering algorithms developed, discusses a novel method to aid in the creation of a ground truth for event detection purposes, and analyses how well the algorithms improve over baseline. We find that assigning semantic information to every individual tweet results in just a worse performance in F1 measure compared to baseline. If however semantics are assigned on a coarser, hashtag level the improvement over baseline is substantial and significant in both precision and recall.

The popularity of microblogging systems has resulted in a new form of Web datamicrotext-which is very different from conventional well-written text. Microtext often has the characteristics of informality, brevity, and varied grammar, which poses new challenges in applying traditional clustering algorithms to analyze microtext. In this paper, we propose a novel two-phase approach for clustering streaming microtext, in particular Twitter messages, into event-based clusters. In the online phase, an incremental process is applied to discover base clusters and maintain detailed summary statistics. Upon demand for any user-specified time horizons, an offline phase is triggered to merge related clusters together. We demonstrate that our proposed approach can achieve better clustering accuracy than state-ofthe-art methods.

J. Inf. Process. Syst., 2018

Twitter is one of the most popular social platforms for online users to share trendy information and views on any event. Twitter reports an event faster than any other medium and contains enormous information and views regarding an event. Consequently, Twitter topic summarization is one of the most convenient ways to get instant gist of any event. However, the information shared on Twitter is often full of nonstandard abbreviations, acronyms, out of vocabulary (OOV) words and with grammatical mistakes which create challenges to find reliable and useful information related to any event. Undoubtedly, Twitter event summarization is a challenging task where traditional text summarization methods do not work well. In last decade, various research works introduced different approaches for automatic Twitter topic summarization. The main aim of this survey work is to make a broad overview of promising summarization approaches on a Twitter topic. We also focus on automatic evaluation of summ...

RANLP 2017 - Recent Advances in Natural Language Processing Meet Deep Learning, 2017

In this paper, we propose an approach to build a timeline with actions in a sports game based on tweets. We combine information provided by external knowledge bases to enrich the content of the tweets, and apply graph theory to model relations between actions and participants in a game. We demonstrate the validity of our approach using tweets collected during the EURO 2016 Championship and evaluate the output against live summaries produced by sports channels.

International Journal of Rough Sets and Data Analysis, 2016

Summary generation is an important process in those conditions where the user needs to obtain the key features of the document without having to go through the whole document itself. The summarization process is of basically two types: 1) Single document Summarization and, 2) Multiple Document Summarization. But here the microblogging environment is taken into account which have a restriction on the number of characters contained within a post. Therefore, single document summarizers are not applicable to this condition. There are many features along which the summarization of the microblog post can be done for example, post's topic, it's posting time, happening of the event, etc. This paper proposes a method that includes the temporal features of the microblog posts to develop an extractive summary of the event from each and every post, which will further increase the quality of the summary created as it includes all the key features in the summary.

This paper explores the real-time summarization of scheduled events such as soccer games from torrential flows of Twitter streams. We propose and evaluate an approach that substantially shrinks the stream of tweets in real-time, and consists of two steps: (i) sub-event detection, which determines if something new has occurred, and (ii) tweet selection, which picks a representative tweet to describe each sub-event. We compare the summaries generated in three languages for all the soccer games in Copa America 2011 to reference live reports offered by Yahoo! Sports journalists. We show that simple text analysis methods which do not involve external knowledge lead to summaries that cover 84% of the sub-events on average, and 100% of key types of sub-events (such as goals in soccer). Our approach should be straightforwardly applicable to other kinds of scheduled events such as other sports, award ceremonies, keynote talks, TV shows, etc.

ABSTRACT Social media services such as Twitter generate phenomenal volume of content for most real-world events on a daily basis. Digging through the noise and redundancy to understand the important aspects of the content is a very challenging task. We propose a search and summarization framework to extract relevant representative tweets from an unfiltered tweet stream in order to generate a coherent and concise summary of an event.