Pre-scheduled events, such as TV shows and sports games, usually garner considerable attention from the public. Twitter captures large volumes of discussions and messages related to these events, in real-time. Twitter streams related to pre-scheduled events are characterized by the following: (1) spikes in the volume of published tweets reflect the highlights of the event and (2) some of the published tweets make reference to the characters involved in the event, in the context in which they are currently portrayed in a subevent. In this paper, we take advantage of these characteristics to identify the highlights of pre-scheduled events from tweet streams and we demonstrate a method to summarize these highlights. We evaluate our algorithm on tweets collected around 2 episodes of a popular TV show, Game of Thrones, Season 7.Published versio

Andy, Anietie

Callison-Burch, Chris

Wijaya, Derry Tanti

English

Boston University Institutional Repository (OpenBU)

Boston UniversityOpenBU http://open.bu.eduComputer Science BU Open Access Articles2019Winter is here: summarizing Twitterstreams related to pre-scheduledeventsThis work was made openly accessible by BU Faculty. Please share how this access benefits you.Your story matters.Version Published versionCitation (published version): Anietie Andy, Derry Tanti Wijaya, Chris Callison-Burch. 2019. "Winteris here: Summarizing Twitter Streams related to Pre-ScheduledEvents." Proceedings of the Second Workshop on Storytelling.Proceedings of the Second Workshop on Storytelling. 2019-08 -2019-08. https://doi.org/10.18653/v1/w19-3412https://hdl.handle.net/2144/40711Boston University“Winter is here”: Summarizing Twitter Streams related to Pre-ScheduledEventsAnietie Andyandyanietie@gmail.comUniversity of PennsylvaniaDerry Wijayawijaya@bu.eduBoston UniversityChris Callison-Burchccb@cis.upenn.eduUniversity of PennsylvaniaDaphne Ippolitodaphnei@seas.upenn.eduUniversity of PennsylvaniaAbstractPre-scheduled events, such as TV shows andsports games, usually garner considerable at-tention from the public. Twitter captures largevolumes of discussions and messages relatedto these events, in real-time. Twitter streamsrelated to pre-scheduled events are charac-terized by the following: (1) spikes in thevolume of published tweets reflect the high-lights of the event and (2) some of the pub-lished tweets make reference to the charac-ters involved in the event, in the context inwhich they are currently portrayed in a sub-event. In this paper, we take advantage ofthese characteristics to identify the highlightsof pre-scheduled events from tweet streamsand we demonstrate a method to summarizethese highlights. We evaluate our algorithm ontweets collected around 2 episodes of a popu-lar TV show, Game of Thrones, Season 7.1 IntroductionEvery week, pre-scheduled events, such as TVshows and sports games capture the attention ofvast numbers of people. The first episode of theseventh season of Game of Thrones (GOTS7), apopular fantasy show on HBO, drew in about 10million viewers during its broadcast1.During the broadcast of a popular pre-scheduledevent, Twitter users generate a huge amountof time-stamped tweets expressing their excite-ments/frustrations, opinions, and commentingabout the characters involved in the event, in thecontext in which they are currently portrayed in asub-event. For example, the following are sometweets that were published during a three minutetime period of an episode of GOTS7:• Bend the knee... Jon snow #gots71https://en.wikipedia.org/wiki/Game_of_Thrones_(season_7)#Ratings• finally Jon and Dany meet and i’m freakingout #gots7• Daenerys: have you not come to bend theknee? Jon Snow: i have not Daenerys.These tweets reflect a part of or the whole scenethat happened during this time period on the showi.e. Jon Snow meeting with Daenerys.Monitoring tweet streams - related to an event,for information related to sub-events can be timeconsuming partly because of the overwhelmingamount of data, some of which are redundant or ir-relevant to the sub-event. In this paper, we proposea method to summarize tweet streams related topre-scheduled events. We aim to identify the high-lights of pre-scheduled events from tweet streamsrelated to the event and automatically summarizethese highlights. Specifically we evaluate our al-gorithm on tweets we collected around a popularfantasy TV show, Game of Thrones. We will makethis dataset available to the research community 2.This paper makes the following contributions:• Identify the highlights of pre-scheduledevents from tweets streams related to theevent and identify the character that had themost mentions in tweets published during thehighlight.• Identify the context in which this characterwas being discussed in tweets published dur-ing the highlight and summarize the highlightby selecting the tweets that discuss this char-acter in a similar context.2 Related WorkSome approaches to summarizing tweets relatedto an event adapt or modify summarization tech-niques that perform well with documents fromnews articles and apply these adaptations totweets. In Sharifi et al. (2010a); Shen et al.2https://anietieandy.github.io(2013) a graph-based phrase reinforcement algo-rithm was proposed. In Sharifi et al. (2010b) a hy-brid TF-IDF approach to extract one-or-multiple-sentence summary for each topic was proposed.In Liu et al. (2011) an algorithm is proposedthat explores a variety of text sources for sum-marizing twitter topics. In Harabagiu and Hickl(2011) an algorithm is proposed that synthesizescontent from multiple microblog posts on thesame topic and uses a generative model whichinduces event structures from the text and cap-tures how users convey relevant content. In Mar-cus et al. (2011), a tool called ”Twitnfo” was pro-posed. This tool used the volume of tweets re-lated to a topic to identify peaks and summarizethese events by selecting tweets that contain a de-sired keyword or keywords, and selects frequentterms to provide an automated label for each peak.In Takamura et al. (2011); Shen et al. (2013) asummarization model based algorithm was pro-posed based on the facility location problem. InChakrabarti and Punera (2011) a summarizationalgorithm based on learning an underlying hid-den state representation of an event via hiddenMarkov models is proposed. Louis and New-man (2012) proposed an algorithm that aggre-gates tweets into subtopic clusters which are thenranked and summarized by a few representativetweets from each cluster (Shen et al., 2013). InNichols et al. (2012) an algorithm was proposedthat uses the volume of tweets to identify sub-events, then uses various weighting schemes toperform tweet selection. Li et al. (2017) proposedan algorithm for abstractive text summarizationbased on sequence-to-sequence oriented encoder-decoder model equipped with a deep recurrentgenerative decoder. Nallapati et al. (2016) pro-posed a model using attentional endoder-decoderRecurrent Neural Network.Our algorithm is different from the previouswork in that it identifies the character that had themost mentions in tweets published in a highlightand identifies the context in which this characterwas being discussed in this highlight; it then sum-marizes the highlight by selecting tweets that dis-cuss this character in a similar context.3 DatasetOur dataset consist of tweets collected around 7episodes of a popular TV show, GOTS7. We al-gorithmically identify points of elevated drama orhighlights from this dataset and summarize thesehighlights.Each episode of GOTS7 lasted approximatelyan hour. We used the Twitter streaming API to col-lect time-stamped and temporally ordered tweetscontaining ”#gots7”, a popular hashtag for theshow, while each episode was going on. We notethat filtering by hashtag gives us only some of thetweets about the show–we omit tweets that usedother GOTS7 related hashtags or no hashtags atall. Our dataset consists of the tweet streams forseven episodes of GOTS7; we collected the fol-lowing number of tweets: 32,476, 9,021, 4,532,8,521, 6,183, 8,971, and 17,360 from episodes1,2,3,4,5,6, and 7 respectively.3.1 Highlight IdentificationTo identify the highlights of each episode, weplot the number of tweets that were published perminute for each minute of an episode. Since dataat the minute level is quite noisy and to smooth outshort-term fluctuations, we calculated the mean ofthe number of tweets published every 3 minutesas shown by the red line in Figure 1, which formspeaks in the tweet volume. We observed the fol-lowing: (1) the spikes in the volume of tweetscorrespond to some exciting events/scenes duringthe show and (2) when there is a spike in the vol-ume of tweets, the characters involved in the sub-events (around that time period) spike in popu-larity as well in the published tweets. For exam-ple, in episode 1, when the character Arya Starkwore Walder Freys face and poisoned all of houseFrey, there was a spike in the volume of tweetsat this time period; also Arya Stark and WalderFrey spiked in popularity in tweets published inthis time period.Several studies have suggested that a peak needsto rise above a threshold to qualify it as a high-light in a given event. Hence, similar to Shammaet al. (2009); Gillani et al. (2017), we identifyhighlights of the events by selecting the peaks us-ing the mean and standard deviation of the peaksin all the tweets collected around the 7 episodes ofGOTS7.3.2 Character IdentificationTo identify the characters involved in GOTS7, weselect all the character names listed in the GOTS7Wikipedia page. It is common for tweets to men-tion nicknames or abbreviations rather that char-acter full names. For example, in tweets col-Figure 1: Histogram of number of tweets published per minute during an episode of Game of Thrones. The redline, which forms peaks, shows the mean of the number of tweets published every 3 minutes. The names of thecharacter that had the most mentions during each peak in tweet volume are also shown.lected around GOTS7 episode 1, the characterSandor Clegane is mentioned 22 times by his fullname and 61 times by his nickname “the hound.”Therefore, for each character, we assemble a listof aliases consisting of their first name (whichfor GOTS7 characters is unique), and the nick-names listed in the first paragraph of the charac-ter’s Wikipedia article. All characters end up hav-ing at most 2 aliases i.e. their first name and/or anickname. For example, the nicknames for SandorClegane are Sandor and the hound.To identify the character(s) involved in a highlightfrom the tweets published during the highlight, wedo the following: (1) given the highlight (section3.1) we count the frequency of mentions of char-acters in tweets published during the highlight.We select the character with the most mentions.The intuition here is that the character with themost mentions in tweets published in each high-light played a major role in the sub-event that oc-curred during the highlight.4 Our ModelWe use context2vec Melamud et al. (2016) to cre-ate a vector representation for the tweets in eachhighlight. Context2vec uses an unsupervised neu-ral model, a bidirectional LSTM, to learn sen-tential context representations that result in com-parable or better performances on tasks such assentence completion and lexical substitution thanpopular context representation of averaged wordembeddings. Context2vec learns sentential con-text representation around a target word by feed-ing one LSTM network with the sentence wordsaround the target from left to right, and anotherfrom right to left. These left-to-right and right-to-left context word embeddings are concatenatedand fed into a multi-layer perceptron to obtainthe embedding of the entire joint sentential con-text around the target word. Finally, similar toword2vec, context2vec uses negative sampling toassign similar embeddings to this sentential con-text and its target word. This process indirectlyresults in sentential contexts, which are associatedwith similar target words, being assigned similarembeddings.Given the tweets that mention the character thathad the most mentions in tweets published dur-ing the time period of a highlight, we want vectorrepresentations of tweets that represent the con-text in which this character is discussed in thesetweets. Tweets that discuss the character in asimilar context should have similar vector repre-sentations. The sentential context representationlearned by context2vec is used to find the tweetsthat best summarize the highlight.We cluster the context2vec vectors using K-Means. To identify the number of clusters forEpisode Scene Description Tweet SummaryEpisode 3 Daenerys Targaryen meetsJon Snow(1) Jon snow tellingDaenerys about the whitewalkers(2) Davos keeping it straightand simple for team Jonsnow, the king in the north!(3) Daenerys Targaryen andJon snow... in the same.room.Episode 4Arya displays superb fencingskills as Littlefinger andSansa watch(1) Arya got Sansa andLittlefinger shook!(2) Arya and Briennefighting each other,marvellous piece of telly.(3) so what i’m getting fromthis is that Arya can beat thehound.Table 1: Example highlight summaries of Game of Thrones episode 3 and 4tweets published during the time period of a high-light, we use the elbow method (Kodinariya andMakwana, 2013). For each cluster we choose thefive tweets closest to their respective cluster cen-troids as the tweets that summarize the highlight;these five tweets were concatenated together. Wevaried the number of tweets we concatenated andfive gave the optimal results.5 ExperimentsWe evaluate our algorithm on tweets collectedaround 2 episodes of GOTS7 i.e. episodes 3 and 4.The plot summaries of these GOTS7 episodes areavailable on the IMDB website. We collected theplot summaries for GOTS7 episodes 3 and 4 fromthe IMDB website. We compared the summariesfrom our model to the plot summaries of theseepisodes from IMDB using the ROUGE metric(Lin, 2004). We compared our model to 5 com-petitive summarization algorithms and our modelperformed better than all the baselines in bothepisodes 3 and 4 as shown int tables 2 and 3. Table1 shows some of the summaries from our modelfor a highlight in both episodes 3 and 4.6 BaselinesLexRank: Computes the importance of tex-tual units using eigenvector centrality on a graphrepresentation based on the similarity of theunits (Erkan and Radev, 2004).TextRank: A graph-based extractive summariza-tion algorithm (Mihalcea and Tarau, 2004).LSA: Constructs a terms-by-units matrix, and es-timates the importance of the textual units basedon SVD on the matrix (Gong and Liu, 2001)Luhn: Derives a significance factor for each tex-tual unit based occurrences and placements of fre-quent words within the unit (Luhn, 1958)Most Retweeted: We select the tweet with themost number of re-tweets in an highlight as a sum-mary of the highlight.Algorithms Rouge-1 Rouge-2 Rouge-LOur Model 38 10 31.5Luhn 14.4 5 12.3Most Re-tweets 10 3 10TextRank 15 2 11.7LexRank 10 2 10LSA 10 0 10Table 2: ROUGE-F1 scores on tweets from highlightsin Episode 3Algorithms Rouge-1 Rouge-2 Rouge-LOur Model 33 19.4 25.2Luhn 16.5 0 13Most Re-tweets 10 4 10TextRank 10.5 4 10LexRank 10 5 10LSA 11.3 5 10Table 3: ROUGE-F1 scores on tweets from highlightsin Episode 47 Conclusion and Future WorkWe proposed a model to summarize highlights ofevents from tweet streams related to the events andshowed that our model outperformed several base-lines. In the future, we will test our model ontweets collected around other events such as Pres-idential debates. There were a few cases whereour algorithm generated summaries that are some-what similar, for example: ”Arya got Sansa andLittlefinger shook!” and ”Littlefinger is shook byAryas fighting skills”. In the future, we will im-prove the diversity of the generated summaries.ReferencesDeepayan Chakrabarti and Kunal Punera. 2011. Eventsummarization using tweets. ICWSM, 11:66–73.Gu¨nes Erkan and Dragomir R Radev. 2004. Lexrank:Graph-based lexical centrality as salience in textsummarization. Journal of artificial intelligence re-search, 22:457–479.Mehreen Gillani, Muhammad U Ilyas, Saad Saleh,Jalal S Alowibdi, Naif Aljohani, and Fahad SAlotaibi. 2017. Post summarization of microblogsof sporting events. In Proceedings of the 26th In-ternational Conference on World Wide Web Com-panion, pages 59–68. International World Wide WebConferences Steering Committee.Yihong Gong and Xin Liu. 2001. Generic text summa-rization using relevance measure and latent semanticanalysis. In Proceedings of the 24th annual inter-national ACM SIGIR conference on Research anddevelopment in information retrieval, pages 19–25.ACM.Sanda M Harabagiu and Andrew Hickl. 2011. Rele-vance modeling for microblog summarization. InICWSM.Trupti M Kodinariya and Prashant R Makwana. 2013.Review on determining number of cluster in k-means clustering. International Journal, 1(6):90–95.Piji Li, Wai Lam, Lidong Bing, and Zihao Wang.2017. Deep recurrent generative decoder forabstractive text summarization. arXiv preprintarXiv:1708.00625.Chin-Yew Lin. 2004. Rouge: A package for auto-matic evaluation of summaries. Text SummarizationBranches Out.Fei Liu, Yang Liu, and Fuliang Weng. 2011. Why issxsw trending?: exploring multiple text sources fortwitter topic summarization. In Proceedings of theWorkshop on Languages in Social Media, pages 66–75. Association for Computational Linguistics.Annie Louis and Todd Newman. 2012. Summariza-tion of business-related tweets: A concept-based ap-proach. Proceedings of COLING 2012: Posters,pages 765–774.Hans Peter Luhn. 1958. The automatic creation of lit-erature abstracts. IBM Journal of research and de-velopment, 2(2):159–165.Adam Marcus, Michael S Bernstein, Osama Badar,David R Karger, Samuel Madden, and Robert CMiller. 2011. Twitinfo: aggregating and visualizingmicroblogs for event exploration. In Proceedings ofthe SIGCHI conference on Human factors in com-puting systems, pages 227–236. ACM.Oren Melamud, Jacob Goldberger, and Ido Dagan.2016. context2vec: Learning generic context em-bedding with bidirectional lstm. In CoNLL, pages51–61.Rada Mihalcea and Paul Tarau. 2004. Textrank: Bring-ing order into text. In Proceedings of the 2004 con-ference on empirical methods in natural languageprocessing.Ramesh Nallapati, Bowen Zhou, Caglar Gulcehre,Bing Xiang, et al. 2016. Abstractive text summa-rization using sequence-to-sequence rnns and be-yond. arXiv preprint arXiv:1602.06023.Jeffrey Nichols, Jalal Mahmud, and Clemens Drews.2012. Summarizing sporting events using twitter. InProceedings of the 2012 ACM international confer-ence on Intelligent User Interfaces, pages 189–198.ACM.David A Shamma, Lyndon Kennedy, and Elizabeth FChurchill. 2009. Tweet the debates: understandingcommunity annotation of uncollected sources. InProceedings of the first SIGMM workshop on Socialmedia, pages 3–10. ACM.Beaux Sharifi, Mark-Anthony Hutton, and Jugal Kalita.2010a. Summarizing microblogs automatically. InHuman Language Technologies: The 2010 AnnualConference of the North American Chapter of theAssociation for Computational Linguistics, pages685–688. Association for Computational Linguis-tics.Beaux Sharifi, Mark-Anthony Hutton, and Jugal KKalita. 2010b. Experiments in microblog summa-rization. In Social Computing (SocialCom), 2010IEEE Second International Conference on, pages49–56. IEEE.Chao Shen, Fei Liu, Fuliang Weng, and Tao Li. 2013.A participant-based approach for event summariza-tion using twitter streams. In Proceedings of the2013 Conference of the North American Chapter ofthe Association for Computational Linguistics: Hu-man Language Technologies, pages 1152–1162.Hiroya Takamura, Hikaru Yokono, and Manabu Oku-mura. 2011. Summarizing a document stream.In European conference on information retrieval,pages 177–188. Springer.

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