Summarizing microblog stream

Transcription

1 SIG-SWO-A Summarizing microblog stream Hiroya Takamura Hikaru Yokono Manabu Okumura Tokyo Institute of Technology Precision and Intelligence Laboratory Abstract: We address the task of summarizing numerous short documents on microblogs including Twitter. On microblogs, thousands of short documents on a certain topic such as sports games or TV dramas are posted by users. Noticeable characteristics of microblog data are that documents are often very highly redundant and are aligned on timeline. There can be dozens of documents on one event in the topic. Two very similar documents will refer to two distinct events when the documents are temporally distant. We examine the microblog data to gain more understanding of those characteristics, and propose a summarization model for numerous short documents on timeline, along with an approximate fast algorithm for generating summary. We empirically show that our model generates a good summary on the dataset of microblog documents on sports games. 1 Twitter 1 Twitter tweet takamura@pi.titech.ac.jp 1 Ustream 2 [6]

2 10 30 [12] Twitter Streaming API tweet( ) : ; (i) 03-2

3 2: (ii) 3 p- Takamura [12] [2] ( p ) ( ) p- Takamura ( ) ( ) e ij d i d j 3 z ij d j d i 1 0 i,j e ijz ij p x i d i 1 0 i x i p p- max. i,j e ijz ij s.t. z ij x i ; i, j, (1) i x i p, (2) i z ij = 1; j, (3) z ii = x i ; i, (4) x i {0, 1}; i, (5) z ij {0, 1}; i, j. (6) (1) (2) (3) (4) z ij (6) NP [3] e ij d i d j Takamura [12] e ij e ij = d i d j. (7) d j d i d i d j d i d j

4 2 ; (i) (ii) e ij 3: 4.1 e ij 0.5 t(d i) t(d j ) /β. (8) t(d) d ( ) β β β 1/c i c i d i e time ij e time ij = e ij c i 0.5 t(d i) t(d j ) /β. (9) 4.2 p- p- 3 4: 4 p 1 p p- max. i,j e ijz ij s.t. z ij x i ; i, j, i c ix i p, i z ij = 1; j, z ii = x i ; i, z ij z ik ; i, j, k(j k i) (10) z ij z ik ; i, j, k(i k j) (11) x i {0, 1}; i, z ij {0, 1}; i, j. 03-4

5 (10) (11) p- p- k- p d m1,, d mp i j, t(d mi ) t(d mj ) while for l = 1 to p d ml +1,, d ml+1 1 d ml d ml+1 end for for l = 1 to p d ml end for end while h max h max = h argmax h:ml h<m l+1 e ml j + j=m l m l+1 j=h+1 e ml+1 j, d hmax d ml d ml+1 h max h + 1 h e ml h+1 e ml+1 h+1 d ml 5 Sharifi [10] O Connor [7] Twitter tweet( ) Swan and Jensen [11] O Connor Swan and Jensen Topic Detection and Tracking (TDT) [1] Topic Detection and Tracking (TDT) [1] TDT TDT [5] [8, 9] 03-5

6 6 6.1 ROUGE[4] ROUGE ROUGE ROUGE ROUGE 10 ROUGE ( ) ROUGE ( ) 4 tweet Twitter Streaming API tweet( ) % 2010 FIFA ( ) Streaming API (#soccer, #jfa, #wc2010, #jfa2010, #daihyo, #2010wc) 6 ( 1 Streaming API statuses/sample 7 5% Streaming API statuses/filter

7 1: FIFA : MeCab e ij e time ij 6.4 ILOG CPLEX version 11.1 p- 7 8 p- p- e ij e time ij e time ij β / / / 9 (4.1 ) β ( ) e ij Takamura [12] p- e ij e time ij β = p random: p p ROUGE equal: p e time ij 03-7

8 3: p- ROUGE e ij e time ij β = 300 β = 600 β = β = e time ij β 19:42:24 19:58:18 20:19:27 20:39:30 20:52:00 20:58:55 21:00:56 19:19:39 19:39:56 19:49:18 20:03:41 20:26:27 20:32:02 20:36:29 21:08:58 p- 4.3 p- 4 p- ROUGE p- e time ij 1/c i equal 0.92 p Twitter Ustream 10 Perl

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