Conference Paper

Social context summarization.

DOI: 10.1145/2009916.2009954 Conference: Proceeding of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2011, Beijing, China, July 25-29, 2011
Source: DBLP


We study a novel problem of social context summarization for Web documents. Traditional summarization research has focused on extracting informative sentences from standard documents. With the rapid growth of online social networks, abundant user generated content (e.g., comments) associated with the standard documents is available. Which parts in a document are social users really caring about? How can we generate summaries for standard documents by considering both the informativeness of sentences and interests of social users? This paper explores such an approach by modeling Web documents and social contexts into a unified framework. We propose a dual wing factor graph (DWFG) model, which utilizes the mutual reinforcement between Web documents and their associated social contexts to generate summaries. An efficient algorithm is designed to learn the proposed factor graph model.Experimental results on a Twitter data set validate the effectiveness of the proposed model. By leveraging the social context information, our approach obtains significant improvement (averagely +5.0%-17.3%) over several alternative methods (CRF, SVM, LR, PR, and DocLead) on the performance of summarization.

Download full-text


Available from: Zhong Su,
43 Reads
  • Source
    • "Intuitively, the more often some part of the story is tweeted, the more salient it might be. Previous work assumed that such socially focused sentences might be closely related to the reference summary [18] [3] [16]. However, there are some important questions left unanswered. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Single-document summarization is a challenging task. In this paper, we explore effective ways using the tweets linking to news for generating extractive summary of each document. We reveal the very basic value of tweets that can be utilized by regarding every tweet as a vote for candidate sentences. Base on such finding, we resort to unsupervised summarization models by leveraging the linking tweets to master the ranking of candidate extracts via random walk on a heterogeneous graph. The advantage is that we can use the linking tweets to opportunistically " supervise " the summa-rization with no need of reference summaries. Furthermore, we analyze the influence of the volume and latency of tweets on the quality of output summaries since tweets come after news release. Compared to truly supervised summarizer unaware of tweets, our method achieves significantly better results with reasonably small tradeoff on latency; compared to the same using tweets as auxiliary features, our method is comparable while needing less tweets and much shorter time to achieve significant outperformance.
    The 38th Annual ACM SIGIR Conference on Research and Development in Information Retrieval, Sandiago, Chile; 08/2015
  • Source
    • "In addition, different types of features have been used, including lexical, acoustic and structural characteristics (Xie et al., 2008; Maskey and Hirschberg, 2005). Recent works have been focused on adapting summarization to the social context, exploiting user generated contents associated with the documents (Yang et al., 2011; Hu et al., 2012). Implicit and explicit community feedback in online collaborative websites have also been leveraged to detect highlights of media assets (San Pedro et al., 2009). "
    [Show abstract] [Hide abstract]
    ABSTRACT: This papers presents a context-aware NLP approach to automatically detect noteworthy information in spontaneous mobile phone conversations. The proposed method uses a supervised modeling strategy which considers both features from the content of the conversation as well as contextual information from the call. We empirically analyze the predictive performance of features of different nature on a corpus of mobile phone conversations. The results of this study reveal that the context of the conversation plays a crucial role on boosting the predictive performance of the model.
    COLING, Dublin,Ireland; 01/2014
  • Source
    • "Recently, significant attention has been directed at detecting and tracking events as they surface in microblogs [12] [27]. Social media messages have also been used to predict the popularity aspect of news stories [3], as well as assist in their summarization [33]. Our problem statement looks at the reverse perspective by observing responses to news articles describing real-world events. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Social media responses to news have increasingly gained in importance as they can enhance a consumer's news reading experience, promote information sharing and aid journalists in assessing their readership's response to a story. Given that the number of responses to an online news article may be huge, a common challenge is that of selecting only the most interesting responses for display. This paper addresses this challenge by casting message selection as an optimization problem. We define an objective function which jointly models the messages' utility scores and their entropy. We propose a near-optimal solution to the underlying optimization problem, which leverages the submodularity property of the objective function. Our solution first learns the utility of individual messages in isolation and then produces a diverse selection of interesting messages by maximizing the defined objective function. The intuitions behind our work are that an interesting selection of messages contains diverse, informative, opinionated and popular messages referring to the news article, written mostly by users that have authority on the topic. Our intuitions are embodied by a rich set of content, social and user features capturing the aforementioned aspects. We evaluate our approach through both human and automatic experiments, and demonstrate it outperforms the state of the art. Additionally, we perform an in-depth analysis of the annotated ``interesting'' responses, shedding light on the subjectivity around the selection process and the perception of interestingness.
    Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining; 08/2013
Show more