Runtime summarization of tweets during disaster.

Transcription

1 Runtime summarization of tweets during disaster. SNLP TERM PROJECT Group-14 GROUP MEMBERS: 1. DEVASHISH 12CS NISHANT SINGH 12CS SHUBHAM GADIA 12CS ABHISHEK KUMAR 12CS DINESH BETHEKAR 12CS30008 Mentor- Koustav Rudra

2 Aim: Large amount of tweets are posted soon after disaster. Aim is to produce the summary of the current situation by dynamically updating the summary with the current stream of tweets.

3 Dataset: Tweets related to terrorist attack in Gurdaspur. 1. about processed english tweets. 2. about 2400 processed hindi tweets. Tweets related to bomb blast in kuwait mosque. about processed english tweets. Tweets related to Harda train accident 1. about 4000 processed english tweets. 2. about 300 processed hindi tweets. Tweets related to Tenesse attack about processed english tweets.

4 Tagging English tweets into situational and non situational tweets : Gurdaspur English Dataset : Total processed tweets : Total situational tweets : 8253 %age of situational tweets : 36% Kuwait English Dataset : Total processed tweets : Total situational tweets : %age of situational tweets : 85%

5 Importance of Hindi tweets : Harda Hindi Dataset corrected : Total processed tweets : 227 Total situational tweets : 119 Tweets exclusively in Hindi : 16 Tweets in Hindi and English both but latest in Hindi : 13 %age of situational tweets : 52% %age of important situational tweets : 24% %age of Hindi exclusive tweets : 13% %age of Latest Hindi tweets : 11%

6 Importance of Hindi tweets : Gurdaspur Hindi Dataset : Total processed tweets : 2358 Total situational tweets : 396 Tweets exclusively in Hindi : 55 Tweets in Hindi and English both but latest in Hindi : 65 %age of situational tweets : 16.8% %age of important situational tweets : 30% %age of Hindi exclusive tweets : 13.8% %age of Latest Hindi tweets : 16.4%

7 Approach 1 : Manually classifying the processed tweets into situational and non situational tweets. Situational tweets to be used. Traversing the tweets in order of timeline and generating summary dynamically to simulate tweets stream. Appropriately weighting the newer tweets and reducing weights of older tweets. When new tweet is found add to one of the above cluster. Run some standard summarisation algo and generate the summary.

8 Implementation: Algorithm overview - Generating word graph: Each tweet is divided into bigrams. Each node in the graph is a bigram. Two nodes have an edge if they share one of the word Weights are added to nodes and edges in order to store the count for each bigram or trigram. The weight of bigram increases as they increase in frequency of occurence - >more of same tweet is retweeted resulting in greater chance to be in summary Bigrams of newer tweets have greater weight. This is done by decreasing the weight of bigrams of older tweets by factor of (1-c) t where c is decay constant and t is seconds passed between the last occurence of that bigram and now.

9 Generating Summary: Highest weighted bigram is taken. We move from bigram to bigram in both the direction to reach the start and end node. Next node is chosen by following scoring function among its neighbours:

10 Scoring function score(n, e, m, p, k) = c1 frequency(n) + c2 edge score(e, m)) + c3 word score(n, p) + c4 word score(n, k) c5 frequent word pen(n) p - partial summary n - node adjacent to one of the path s endpoints m by edge e k - list of keywords related to an event The next bigram with most score is used and added to list. From nodes in this list new bigrams are taken. On reaching start and end node a summary is generated. This is run thrice after another greatest weight bigram is selected not present in previous summaries.

11 Improvement - Approach 2 : Manually classifying the processed tweets into situational and non situational tweets. Situational tweets to be used. Collect small number of tweets and create initial cluster. When new tweet is found we try to add to one of the above cluster whose centroid is closest to the tweet. If the closest cluster is very distant to the tweet we create a new cluster. To delete old clusters we calculate the average arrival time of the last p percent of tweets in a cluster and if it is more than three day old we remove it. Run some standard summarisation algo and generate the summary by taking tweets from the cluster according to the appropriate weight of the clusters. Write manual summary of appropriate size for further processing. Run Rouge Score Algorithm on generated and the manual summary for the validation of summary generated.

12 Challenges : Time and memory requirement as the graph grew larger. Had to preprocess the tweets to remove special characters.

13 Input and Output

14 INPUT For olariu summarization: gurdaspur

15 Olariu summarization :Output for GurdaspurAttack #gurdaspurattack time for india to recognize a government of balochistan in exile curtail diplomatic ties with pakistan. it is time india developed some appetite for war against the pakistani jihadist state. bsf calls high level meeting in jodhpur after #gurdaspurattack. #gurdaspurattack it is time india developed some appetite for war against the pakistani jihadist state. all terrorists declared dead. chants of vande mataram by police officers at #dinanagar station as all terrorists declared dead. 3 civilians 4 police personnel and 2 militants killed until now in #gurdaspurattack. 3 civilians & 2 convicts inside lockup have been killed in gunbattle with security forces. home ministry confirms 2 gps 3 ak47 10 magazines 2 china made grenades recovered mha sources. they had chinese weapons punjab dgp sumedh singh saini on #gurdaspurattack. 2 attacks playing on media pak/isi terrorist attack. the #gurdaspurattack mha sources.

16 Input: Sumblr summarization: English i am on my way to the train accident spot near harda to personally oversee relief & rescue ops along with CS & DG. live, death toll rises to 31 in harda tragedy, cm chouhan visits derailment spot urgent twin train derail in #madhyapradesh madhya pradesh, harda helpline, co/0c4hjvmnnr. live, death toll rises to 31 in harda tragedy, cm chouhan visits accident site #traintragedy. live, death toll rises to 31 in harda train tragedy, cm chouhan visits accident site 24 killed in harda twin train derailment 24 killed in harda twin train derailment. i am on my way to the train accident spot near harda to personally oversee relief & rescue ops along with CS & DG. #kamayaniexpress train tragedy helpline numbers are harda, bhopal, bina, itarsee, killed in harda twin train mishap i am on my way to the train accident spot near harda to personally oversee relief & rescue ops along with CS & DG.

17 Output : Sumblr For English toi tragedy strikes family, 10 members from narsingpur killed in harda twin train mishap, ten members of a fa harda train tragedy, survivor says no first aid, no immediate help provided pib_india pro, railways on harda train accident saddened by the two train accidents near harda. exclusive, harda train tragedy in pictures! i am on my way to the train accident spot near harda to personally oversee relief and rescue operations, tweets cm shivraj singh chouhan. #newsalert trains that have been diverted due to harda #traintragedy live, co/ekgt1bqx1w. railways helpline numbers are harda , bhopal , bina , itarsi , #followme #f2f #ff dozens still missing in harda train tragedy, 300 rescued, more than i am on my way to the train accident spot near harda to personally oversee relief & rescue ops. we regret the derailing of #kamayaniexpress and janata express at harda in mp and pray for safety of passengers. harda twin train mishap, central railway sets up 4 helpline no #elections #achedin #politics.

18 Input: Sumblr summarization: Hindi र ल ह दस क आड़ म र जन त क श आत द वजय न स र श भ पर स ध द ख म य द श म ह ए न ह दस क व डय र ल ह दस पर सय सत श न म ग भ क इ त फ हरद इल ह ब द क र त म बई ज न व ल 19 न क ट बदल म क हरद म न ह दस 24 क म त द ख त व र हरद क पहल त व र ऐस खबर क श अर नह करत ह त क न कर ग य खबर भ जनत तक पह च ईय य हर क म भ ह कर ग? ड यवट क गय न क ल ट भ न त क ल ऐ शन ल व डय म द ख हरद र ल ह दस क जब ल ग क उपर व ल भ क सह र ह क वल य करन ह त र ल म लय म न च व ल भ क य क म? र ल ह दस पर प एम न जत ई स व दन म आवज क ऐल न न ह दस क कह न य य क ज ब न ल ग क ज न स ख ल रह भ रत य र लव हरद न ह दस इल ह ब द क ह पल इन न बर र ल र यम मन ज स ह न हरद क क तक आपद बत य हरद र ल ह दस म द न जत य द ख हरद म य द श क हरद क प स एक ह जगह पर एक क ब द एक द न इन वजह स ह आ हरद म न ह दस!

19 Output : Sumblr For Hindi हरद क 5 ग व क ल ग बन द वद त र ल ह दस पर सय सत श न म ग भ क इ त फ ज म द र क न य कमज र पट रय पर ऐस ह ह ग ह दस? आपब त जब न बच ई 40 ल ग क ज न द ख म य द श म ह ए न ह दस क व डय नम द ब ध अ धक भर ज न स बह र ल क र यसभ म हरद ह दस म मरन व ल क ध ज ल द गई क य व ह 12 बज तक क लए थ गत ज नल व प लय नह न तय ह कह ब रश क वजह स ह आ ह दस बच व क य ज र ह भ न त क ल ऐ शन ल प ढ़ए स एम शवर ज क सबस पहल कस ल ग क ज न स ख ल रह भ रत य र लव य खबर भ जनत तक पह च ईय य हर क म भ ह कर ग? क ह पल इन न बस र व ट कर हरद क पहल त व र पटर पर ब ढ़ क प न द न द घ टन त एक द खद घटन ह म इस न क सभ य य क लय थ न करत ह

20 References: Efficient Online Summarization of Microblogging Streams By Andrei Olariu - Sumblr algorithm :

21 Thank you!