1. Extractive Summarisation via Sentence Removal: Condensing Relevant Sentences into a Short Summary Marco Bonzanini, Miguel Martinez-Alvarez, and Thomas Roelleke Queen Mary University of London {marcob,miguel,thor}@eecs.qmul.ac.uk SIGIR '13

2. Introduction u The main contribution of this paper is the definition of an algorithm for sentence removal, developed to maximise the score between the summary and the original document. u Instead of ranking the sentences and selecting the most important ones, the algorithm iteratively removes unimportant sentences until a desired compression rate is reached. 2

3. Extractive Summarisation (1/4) u The task of extractive summarisation is to select the subset of sentences and to combine them into a summary which better represents the topic. u In order to form the summary, a length limit has to be considered, based on the number of sentences or the number of words. 3

4. Extractive Summarisation (2/4) 4

5. Extractive Summarisation (3/4) 5

6. Extractive Summarisation (4/4) 6

7. Sentence Selection (1/3) 7

8. Sentence Selection (2/3) 8

9. Sentence Selection (3/3) 9

10. Sentence Removal 10

11. Sentence Removal (Cont’d) 11

12. SR Algorithm 12

13. Opinosis Dataset u The Opinosis dataset is a collection of opinion- oriented data, divided into 51 different topics. u Each topic includes a number of sentences (min. 50, max. 575, avg. 139), taken from different reviews from popular review web sites. u For each topic, 4 or 5 golden standard (human- written) summaries are provided. u The golden standard summaries hence present the pivot opinion for each topic, in a concise way (approx. 2 sentences each). u For this reason, the maximum length of the system generated summaries is fixed to two sentences. 13

14. ROUGE Framework u The ROUGE framework is used to provide a quantitative assessment between the candidate summaries and the golden standards. u Specifically, the results for ROUGE-1, ROUGE-2, and ROUGE-SU4 are reported. u This study also reports the results for MEAD, a state-of-the-art extractive summariser based on cluster centroids. u The best overall results are shown in bold, and the best results within the same scoring function are shown in italic. u Best results labelled with a † show that second-best results are outside their 95% confidence interval. 14

15. Results for Recall ROUGE-1ROUGE-2ROUGE-SU4 MEAD49.32 †10.5823.16 † 33.8006.4312.12 18.8403.9903.91 22.3205.7505.43 20.4605.5404.97 37.4609.2913.80 46.0508.6720.10 15.7802.6403.03 15.6001.4402.96 15

16. Results for Precision ROUGE-1ROUGE-2ROUGE-SU4 MEAD09.1601.8401.02 15.2102.7803.00 30.4206.6411.22 29.3907.7810.27 30.7008.3612.10 19.8705.1805.44 09.6401.7701.10 25.3604.6808.72 12.7001.2002.23 16

17. ROUGE-1ROUGE-2ROUGE-SU4 MEAD15.1503.0801.89 19.8003.6604.19 22.8404.8805.47 24.6706.3906.42 24.0306.5006.59 24.3806.2306.31 15.6402.8802.03 19.0103.2804.16 13.3301.2502.11 17

18. ROUGE-1ROUGE-2ROUGE-SU4 MEAD26.2705.4304.34 25.1904.7105.94 20.2004.2904.39 23.0505.9505.66 21.6805.8605.48 29.92 †07.54 †08.28 † 25.3904.7004.16 16.8802.8603.37 14.3801.3402.38 18

19. Conclusions 19

20. THANKS SIGIR’13, July 28–August 1, 2013, Dublin, Ireland. Copyright 2013 ACM 978-1-4503-2034-4/13/07...$15.00. 20