Tweet‐biased summarization

Yulianti, Evi; Huspi, Sharin Hazlin; Sanderson, Mark

doi:10.1002/asi.23496

Cited by 11 publications

(9 citation statements)

References 31 publications

(38 reference statements)

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“…This research is related to a few areas: summarization and controversy analysis on social media. Twitter Summarization: There has been much work on summarizing Twitter postings while most of them focuses on summarizing events [1,4,8,18,20]. Inouye et al [13] compare multiple arXiv:1806.07942v2 [cs.IR] 15 Jul 2018 Table 1: An example of good (left) and bad (right) summary tweets on "Abortion" posted on Nov 4, 2016.…”

Section: Related Workmentioning

confidence: 99%

Explaining Controversy on Social Media via Stance Summarization

Jang

Allan

2018

The 41st International ACM SIGIR Conference on Research &Amp; Development in Information Retrieval

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In an era in which new controversies rapidly emerge and evolve on social media, navigating social media platforms to learn about a new controversy can be an overwhelming task. In this light, there has been significant work that studies how to identify and measure controversy online. However, we currently lack a tool for effectively understanding controversy in social media. For example, users have to manually examine postings to find the arguments of conflicting stances that make up the controversy.In this paper, we study methods to generate a stance-aware summary that explains a given controversy by collecting arguments of two conflicting stances. We focus on Twitter and treat stance summarization as a ranking problem of finding the top k tweets that best summarize the two conflicting stances of a controversial topic. We formalize the characteristics of a good stance summary and propose a ranking model accordingly. We first evaluate our methods on five controversial topics on Twitter. Our user evaluation shows that our methods consistently outperform other baseline techniques in generating a summary that explains the given controversy.

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Section: Related Workmentioning

confidence: 99%

Explaining Controversy on Social Media via Stance Summarization

Jang

Allan

2018

The 41st International ACM SIGIR Conference on Research &Amp; Development in Information Retrieval

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“…For the overlapping information between tweets and news, we can find representative news sentences for corresponding tweets. We agree with the discussion in previous works [80,163] that the points cared about by some people could also be focuses to others. Hence, such information should be summarized which could be valuable and useful to others.…”

Section: Challengessupporting

confidence: 90%

Event-driven data collection and summarization

Zheng¹

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The online social networks have experienced an unprecedented proliferation. Various platforms change the way people learning about information, particularly on ongoing events, which can only be known from mainstream media in the past. The social media platforms have many pleasing properties compared with traditional media: convenient, detailed, fast and interactive. This provides us an opportunity to learn immediate and detailed information about event of interest, which is highly preferred by decision makers and the public especially when emergencies and significant events happen. Keyword search function is provided by social media platforms like Twitter for users to search messages containing the query keyword(s). However, the returned results are piecemeal due to length limitation, could be mixed with irrelevant tweets or are incomplete due to inappropriate query. This calls for research on collecting clean and complete event-related messages from social media platform. In this dissertation, the researches are conducted on Twitter platform. The collected event relevant tweets could be a large First of all, I would like to express my sincere gratitude towards my supervisor Prof. Aixin Sun. Without his patient guidance, encouragement, immense knowledge and advice, I would not be able to finish my Ph.D career. Under his elaborative supervision, I have not only learned the state-of-the-art information retrieval knowledge, but also acquired a conscientious research attitude through critical thinking. The knowledge and methodology I learnt are invaluable to my research, and even to my life. I feel very grateful to Prof. Gao Cong for his introducing me to my supervisor. Without him, I may miss the chance to study in NTU and do research with Prof. Sun. I would also like to express my great thankfulness to SAP for providing me Ph.D scholarship, and the chance to get access to real industry working environment and work with talented colleagues. I appreciate all the supports, guidances and suggestions given

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“…Lately, there is increasing interest in automatically summarize information from short documents, especially from tweets. In this context, we highlight the work of Yulianti, Huspi, and Sanderson (2016). In this study, the authors utilized information from Twitter to select the more important sentences from a web document.…”

Section: Related Workmentioning

confidence: 91%

Authority and priority signals in automatic summary generation for online reputation management

Rodríguez‐Vidal

Carrillo-de-Albornoz

Gonzalo

et al. 2020

Asso for Info Science & Tech

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Online reputation management (ORM) comprises the collection of techniques that help monitoring and improving the public image of an entity (companies, products, institutions) on the Internet. The ORM experts try to minimize the negative impact of the information about an entity while maximizing the positive material for being more trustworthy to the customers. Due to the huge amount of information that is published on the Internet every day, there is a need to summarize the entire flow of information to obtain only those data that are relevant to the entities. Traditionally the automatic summarization task in the ORM scenario takes some in‐domain signals into account such as popularity, polarity for reputation and novelty but exists other feature to be considered, the authority of the people. This authority depends on the ability to convince others and therefore to influence opinions. In this work, we propose the use of authority signals that measures the influence of a user jointly with (a) priority signals related to the ORM domain and (b) information regarding the different topics that influential people is talking about. Our results indicate that the use of authority signals may significantly improve the quality of the summaries that are automatically generated.

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Tweet‐biased summarization

Cited by 11 publications

References 31 publications

Explaining Controversy on Social Media via Stance Summarization

Explaining Controversy on Social Media via Stance Summarization

Event-driven data collection and summarization

Authority and priority signals in automatic summary generation for online reputation management

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