Summarizing Microblogs Automatically

Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the ACL, pages 685–688, Los Angeles, California, June 2010. c⃝2010 Association for Computational Linguistics Summarizing Microblogs Automatically Beaux Sharifi, Mark-Anthony Hutton, and Jugal Kalita University of Colorado at Colorado Springs 1420 Austin Bluffs Parkway Colorado Springs, CO 80918, USA {bsharifi, mhutton, jkalita}@uccs.edu Abstract In this paper, we focus on a recent Web trend called microblogging, and in particular a site called Twitter. The content of such a site is an extraordinarily large number of small textual messages, posted by millions of users, at ran- dom or in response to perceived events or sit- uations. We have developed an algorithm that takes a trending phrase or any phrase specified by a user, collects a large number of posts containing the phrase, and provides an auto- matically created summary of the posts related to the term. We present examples of summa- ries we produce along with initial evaluation. 1 Introduction Since Twitter’s inception in 2006, it has grown at an unprecedented rate. In just four years, the ser- vice has grown to approximately 20 million unique visitors each month with users sending short 140- character messages (known as “tweets”) approx- imately 40 million times a day. While the majority of these tweets are pointless babble or conversa- tional, approximately 3.6% of these posts are top- ics of mainstream news (Pear Analytics, 2009). For example, Twitter has been cited as breaking many important events before traditional media, such as the attacks in Mumbai and the crash of the US Airways flight into the Hudson River. In order to help users sort through the vast num- ber of tweets that occur each day, Twitter.com has added a number of tools. For instance, Twitter’s homepage displays important topics for three dif- ferent ranges of time in order to see what topics are popular. For most topics, users are forced to read through related posts in order to try and understand why a topic is trending. In order to help users fur- ther, Twitter has partnered with the third-party website WhatTheTrend1 in order to provide defini- tions of trending topics. WhatTheTrend allows users to manually enter descriptions of why a topic is trending. Unfortunately, WhatTheTrend suffers with spam and rants as well as lag time before a new trending topic is defined by a user. While WhatTheTrend is a step in the right direc- tion, a better approach is to automatically summar- ize important events as they occur in real time. We have developed such a method. Our method can automatically summarize a collection of micro- blogging posts that are all related to a topic into a short, one-line summary. Our results show that our automated summarizer produces summaries that are close to human-generated summaries for the same set of posts. For example, Table 1 below contains a sample of automatically produced sum- maries for some recently trending topics on Twit- ter. 2 Related Work Some early work focused on summarizing results of …

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