Research Article

TweetSum: Automated News Summarization of Twitter Trends

by  Chaitali Khandekar, Raj Daiya, Rhea Parekh, Kavita Kelkar
journal cover
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 165 - Issue 8
Published: May 2017
Authors: Chaitali Khandekar, Raj Daiya, Rhea Parekh, Kavita Kelkar
10.5120/ijca2017913944
PDF

Chaitali Khandekar, Raj Daiya, Rhea Parekh, Kavita Kelkar . TweetSum: Automated News Summarization of Twitter Trends. International Journal of Computer Applications. 165, 8 (May 2017), 5-8. DOI=10.5120/ijca2017913944

                        @article{ 10.5120/ijca2017913944,
                        author  = { Chaitali Khandekar,Raj Daiya,Rhea Parekh,Kavita Kelkar },
                        title   = { TweetSum: Automated News Summarization of Twitter Trends },
                        journal = { International Journal of Computer Applications },
                        year    = { 2017 },
                        volume  = { 165 },
                        number  = { 8 },
                        pages   = { 5-8 },
                        doi     = { 10.5120/ijca2017913944 },
                        publisher = { Foundation of Computer Science (FCS), NY, USA }
                        }
                        %0 Journal Article
                        %D 2017
                        %A Chaitali Khandekar
                        %A Raj Daiya
                        %A Rhea Parekh
                        %A Kavita Kelkar
                        %T TweetSum: Automated News Summarization of Twitter Trends%T 
                        %J International Journal of Computer Applications
                        %V 165
                        %N 8
                        %P 5-8
                        %R 10.5120/ijca2017913944
                        %I Foundation of Computer Science (FCS), NY, USA
Abstract

In the recent times, content generated on social blogging sites has become an encyclopedic source of information for every concerned topic in the world[4]. These sites have enabled people to contribute to the vastness of the content available on the internet. During such times, the amount of disorganized and repetitive information generated through these platforms has complicated the means to get the key information. The application that we propose takes an input phrase from the user, captures all tweets related to it and uses them to create a summary from the content already available on the internet.

References
  • Dunwei Wen, Geoffrey Marshall (2014), Automatic twitter Topic Summarization [online], Available: http://ieeexplore.ieee.org/document/7023580/#full-text-section
  • David Inouye, Jugal K. Kalita (2011), Comparing Twitter summarization algorithms for Multiple Post Summaries [online], Available: http://ieeexplore.ieee.org/abstract/document/6113128/
  • Mr. G. S. Mane, Mrs. A. R. Kulkarni (2015), Twitter Event Summarization Using Phrase Reinforcement Algorithm and NLP Features [online], Available: http://www.ijritcc.org/download/1423973129.pdf
  • Srishti Sharma, Kanika Aggarwal, Palak Papneja, Saheb Singh (2015), Extraction, summarization and sentiment analysis of trending topics on Twitter, Available: http://ieeexplore.ieee.org/document/7346696/
Index Terms
Computer Science
Information Sciences
No index terms available.
Keywords

Summarization Machine Learning Social media trends.

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