Personal summarization from profile networks

Abstract

Personal profile information on social media like LinkedIn.com and Facebook.com is at the core of many interesting applications, such as talent recommendation and contextual advertising. However, personal profiles usually lack consistent organization confronted with the large amount of available information. Therefore, it is always a challenge for people to quickly find desired information from them. In this paper, we address the task of personal profile summarization by leveraging both textual information and social connection information in social networks from both unsupervised and supervised learning paradigms. Here, using social connection information is motivated by the intuition that people with similar academic, business or social background (e.g., comajor, co-university, and co-corporation) tend to have similar experiences and should have similar summaries. For unsupervised learning, we propose a collective ranking approach, called SocialRank, to combine textual information in an individual profile and social context information from relevant profiles in generating a personal profile summary. For supervised learning, we propose a collective factor graph model, called CoFG, to summarize personal profiles with local textual attribute functions and social connection factors. Extensive evaluation on a large dataset from LinkedIn.com demonstrates the usefulness of social connection information in personal profile summarization and the effectiveness of our proposed unsupervised and supervised learning approaches.

Keywords

natural language processing / machine learning / social networks / personal profile summarization

References

[1] LappasT, PuneraK, SarlosT. Mining tags using social endorsement networks. In: Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval. 2011, 195–204

[2] TanC, LeeL, TangJ, Jiang L, ZhouM , LiP. User-level sentiment analysis incorporating social networks. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2011, 1397–1405

[3] YangS H, LongB, SmolaA, Sadagopan N, ZhengZ H , ZhaH Y. Like like alike: joint friendship and interest propagation in social networks. In: Proceedings of the International Conference on World Wide Web. 2011, 537–546

[4] GuyI, Zwerdling N, RonenI , CarmelD, UzielE. Social media recommendation based on people and tags. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2010, 194–201

[5] WanX J, and Yang J W. Multi-document summarization using clusterbased link analysis. In: Proceedings of the 31st International ACM SIGIR Conference on Research and Development in Information Retrieval. 2008, 299–306

[6] WanX J. Using bilingual information for cross-language document summarization. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics. 2011, 1546–1555

[7] RadevD R, JingH Y, StysM, Tam D. Centroid-based summarization of multiple documents. Information Processing and Management, 2004, 40(6): 919–938

[8] RadevD R, McKeown K R. Generating natural language summaries from multiple on-line sources. Computational Linguistics, 1998, 24(3): 469–500

[9] KupiecJ, Pedersen J, ChenF . A trainable document summarizer. In: Proceedings of the 18th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1995, 68–73

[10] LuhnH P. The automatic creation of literature abstracts. IBM Journal of Research and Development, 1958, 2(2): 159–165

[11] KnightK, MarcuD. Statistics-based summarization—step one: sentence compression. In: Proceedings of the 17th National Conference on Artificial Intelligence and 12th Conference on Innovative Applications of Artificial Intelligence. 2000, 703–710

[12] CelikyilmazA, Hakkani-Tur D. Discovery of topically coherent sentences for extractive summarization. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics. 2011, 491–499

[13] WangH L, ZhouG D. Toward a unified framework for standard and update multi-document summarization. ACM Transactions on Asian Language Information Processing, 2012, 11(2)

[14] GenestP E, Lapalme G. Framework for abstractive summarization using text-to-text generation. In: Proceedings of the Workshop on Monolingual Text-To-Text Generation. 2011, 64–73

[15] BarzilayR, McKeown K R. Sentence fusion for multi-document news summarization. Computational Linguistics, 2005, 31(3): 297–328

[16] KhanA, SalimN. A review on abstractive summarization methods. Journal of Theoretical and Applied Information Technology, 2014, 59(1): 64–72

[17] SaggionH, Lapalme G. Generating indicative-informative summaries with sumUM. Computational Linguistics, 2002, 28(4): 497–526

[18] GanesanK, ZhaiC X, HanJ W. Opinosis: a graph-based approach to abstractive summarization of highly redundant opinions. In: Proceedings of the 23rd International Conference on Computational Linguistics. 2010, 340–348

[19] ShenD, SunJ T, LiH, YangQ, ChenZ. Document summarization using conditional random fields. In: Proceedings of the International Joint Conference on Artifical Intelligence. 2007, 2862–2867

[20] WongK F, WuM L, LiW J. Extractive summarization using supervised and semi-supervised learning. In: Proceedings of the 22nd International Conference on Computational Linguistics. 2008, 985–992

[21] MengX F, WeiF R, LiuX H, Zhou M, LiS J , WangH F. Entity-centric topic-oriented opinion summarization in Twitter. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2012, 379–387

[22] RosenthalS, McKeown K. Age prediction in blogs: a study of style, content, and online-behavior in pre- and post-social media generations. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics. 2011, 763–772

[23] HuP, SunC, WuL F, Ji D H, TengC . Social summarization via automatically discovered social context. In: Proceedings of the International Joint Conference on Natural Language Processing. 2011, 483–490

[24] LeskovecJ, Huttenlocher D, KleinbergJ . Predicting positive and negative links in online social networks. In: Proceedings of the 19th International Conference on World Wide Web. 2010, 641–650

[25] LuY, Tsaparas P, NtoulasA , PolanyiL. Exploiting social context for review quality prediction. In: Proceedings of the 19th International Conference on World Wide Web. 2010, 691–700

[26] GuyI, Zwerdling N, RonenI , CarmelD, UzielE. Social media recommendation based on people and tags. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2010, 194–201

[27] TangW B, ZhuangH L, TangJ. Learning to infer social ties in large networks. In: Proceedings of European Conference on Machine Learning and Principles and Practice of Knowledge Discovery. 2011, 381–397

[28] ZhuangH L, TangJ, TangW B, Lou T C, ChinA , WangX. Actively learning to infer socialties. In: Proceedings of Data Mining and Knowledge Discovery. 2012, 270–297

[29] DongY X, TangJ, WuS, TianJ L, ChawlaN V, Rao J H, CaoH H . Link prediction and recommendation across heterogeneous social networks. In: Proceedings of the 12th IEEE International Conference on Data Mining. 2012, 181–190

[30] YangZ, CaiK K, TangJ, Zhang L, SuZ , LiJ Z. Social context summarization. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2011, 255–264

[31] PageL, BrinS, MotwaniR, Winograd T. The pagerank citation ranking: bringing order to the Web. Technical Report, 1998

[32] TangJ, ZhangY, SunJ M, Rao J H, YuW J , ChenY R, FongA C M. Quantitative study of individual emotional states in social networks. IEEE Transactions on Affective Computing, 2011, 3(2): 132–144

[33] HammersleyJ, Clifford J. Markov field on finite graphs and lattices. 1971, CiteULike: 8970271

[34] LaffertyJ, McCallum A, PereiraF C N . Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of the International Conference on Machine Learning. 2001, 282–289

[35] LinC Y. ROUGE: a package for automatic evaluation of summaries. In:Proceedings of ACL-04 Workshop on Text Summarization Branches Out. 2004

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