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Data Summarization with Social Contexts
2016
Proceedings of the 25th ACM International on Conference on Information and Knowledge Management - CIKM '16
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To tackle these challenges, in this paper, we focus on exploiting social contexts to summarize social data while preserving topics in the original dataset. We take Twitter data as a case study. ...
Existing methods find such subsets with objective functions based on data properties such as representativeness or informativeness but do not exploit social contexts, which are distinct characteristics ...
CONCLUSION AND FUTURE WORK Summarizing social data gives us the opportunity to exploit social contexts for data summarization. ...
doi:10.1145/2983323.2983736
dblp:conf/cikm/ZhuangRHGHA16
fatcat:zwx4vszmlzarparjtiykcrdfwe
Social context summarization
2011
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information - SIGIR '11
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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. ...
Basic Idea In our Twitter data set, each Web document is associated with a social context. ...
doi:10.1145/2009916.2009954
dblp:conf/sigir/YangCTZSL11
fatcat:gkheqh7pgrhopbgjbl7vd2cbbi
Context-Enhanced Personalized Social Summarization
2012
International Conference on Computational Linguistics
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based on his interests inferred from social context implicitly. ...
This work investigates an interesting and challenging task in summarization, i.e., personalized social summarization, which aims to adapt summarization result of a specified document to an intended user ...
Acknowledgments This work was supported by the National Natural Science Foundation of China (No. 61133012, 61173062, 61070082, 61070083, 61070243), the major program of the National Social Science Foundation ...
dblp:conf/coling/HuJTG12
fatcat:n3722fgklbdxdf7bgexupnskbq
From Data Integration towards Knowledge Mediation
[chapter]
2009
Lecture Notes in Computer Science
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awareness, social choice, user goals, abstraction, summarization, ranking ... • "An important requirement we'd like to place on mediators is that they be inspectable by the potential users." • Tools from ...
awareness, social choice, user goals, abstraction, summarization, ranking ... • "An important requirement we'd like to place on mediators is that they be inspectable by the potential users." • Tools from ...
doi:10.1007/978-3-642-04238-6_66
fatcat:mugufijwongy7mv7z2o2mbha4i
Data-Driven Dialogue Systems for Social Agents
[article]
2017
arXiv
pre-print
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with modules that perform tasks such as sentiment and style analysis, topic modeling, and summarization. ...
Our strategy is to analyze and index large corpora of social media data, including Twitter conversations, online debates, dialogues between friends, and blog posts, and then to couple this data retrieval ...
We argue that combining data-driven retrieval with modules for sentiment analysis and style, topic analysis, summarization, paraphrasing, and rephrasing will allow for more human-like social conversation ...
arXiv:1709.03190v1
fatcat:wayuoqst4zcmrbellxlopauine
A multi-criteria context-sensitive approach for social image collection summarization
2018
Sadhana (Bangalore)
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In this paper, we propose a multi-criteria context-sensitive approach for social image collection summarization. ...
Social images; social networks; image collection summarization; ontology and knowledge based systems. ...
Social and semantic data have been used in other areas of multimedia data such as video retrieval [21] video summarization [22] . ...
doi:10.1007/s12046-018-0908-9
fatcat:lgn7g6hokfed3f7zhmb6moqoxy
Collective Personal Profile Summarization with Social Networks
2013
Conference on Empirical Methods in Natural Language Processing
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In this paper, we address the task of personal profile summarization by leveraging both personal profile textual information and social networks. ...
Here, using social networks is motivated by the intuition that, people with similar academic, business or social connections (e.g. co-major, co-university, and cocorporation) tend to have similar experience ...
social contexts. ...
dblp:conf/emnlp/WangLKZ13
fatcat:fvwmplkdp5cq3ojejkxgz72ixi
Semantic Linking and Contextualization for Social Forensic Text Analysis
2013
2013 European Intelligence and Security Informatics Conference
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is used for semantically enriching information in a social media context. ...
With the development of social media, forensic text analysis is becoming more and more challenging as forensic analysts have begun to include this information source in their practice. ...
In this subset, combining the baseline with the context extension achieves highest performance. ...
doi:10.1109/eisic.2013.21
dblp:conf/eisic/RenDGKHR13
fatcat:sc7t47wjwzfibhx7ra3lrgtx54
Neural Abstractive Unsupervised Summarization of Online News Discussions
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2106.03953v1 fatcat:sstyzdkr4vbsbm5k5pe2zrysnm
Our novel approach provides a summary that represents the most relevant aspects of a news item that users comment on, incorporating the social context as a source of information to summarize texts in online ...
Social media brings a hurdle to summarization techniques since it requires addressing a multi-document multi-author approach. ...
Mendoza acknowledges funding from the Millennium Institute for Foundational Research on Data. Mr. Mendoza was also funded by ANID PIA/APOYO AFB180002 and ANID FONDECYT 1200211. ...
arXiv:2106.03953v2
fatcat:rnvmfbpm6vhevjbxc3iw3dmhwy
Applying Automatic Text Summarization for Fake News Detection
[article]
2022
arXiv
pre-print
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Apart from the benefit of using automatic text summarization techniques we also find that the incorporation of contextual information contributes to performance gains. ...
The shift to news consumption via social media has been one of the drivers for the spread of misleading and deliberately wrong information, as in addition to it of easy use there is rarely any veracity ...
context data than without. ...
arXiv:2204.01841v1
fatcat:mpx5hq2llzd6noylo5mpsy355u
QUERY-BASED SUMMARIZATION METHODS FOR CONVERSATIONAL AGENTS: AN OVERVIEW
2017
International Journal of Advanced Research in Computer Science
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The data will be in the form of Social media posts, content websites and other user generated text content from which the user shall require tailored information from and about the data. ...
Along with that, the need for a proper framework to mine relevant knowledge from the said data is acknowledged and the challenges that a conversational agent would hence face are identified. ...
Hence, new methods to deal with data must include considerations for context, scale and dynamic nature of learning. ...
doi:10.26483/ijarcs.v8i8.4788
fatcat:tc7jkg32dzem3hvpqt2ydq75iu
Harnessing Popularity in Social Media for Extractive Summarization of Online Conversations
2018
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
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We leverage a popularity measure in social media as a distant label for extractive summarization of online conversations. In social media, users can vote, share, or bookmark a post they prefer. ...
We evaluate the results with ranking metrics, and show that our model outperforms the baseline models which directly use popularity as a measure of informativeness. ...
In previous research, few or no training data was adopted because of a lack of labeled data. Our model harnesses a vast amount of data from social media. ...
doi:10.18653/v1/d18-1144
dblp:conf/emnlp/KanoMTCCO18
fatcat:i7qiiljwkjgtrkteu6ks6qpedm
Improving Social Media Text Summarization by Learning Sentence Weight Distribution
[article]
2017
arXiv
pre-print
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Recently, encoder-decoder models are widely used in social media text summarization. ...
In this way, we encourage our model to focus on the key sentences, which have high relevance with the summary. ...
RNN-context RNN-context is a sequence-tosequence framework with the attention mechanism.
Results and Discussions We compare our approach with baselines, including RNN and RNN-context. ...
arXiv:1710.11332v1
fatcat:4huayrkmw5cnpigs276lll4ymu
TLDR9+: A Large Scale Resource for Extreme Summarization of Social Media Posts
[article]
2021
arXiv
pre-print
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While most existing summarization corpora contain data in the order of thousands to one million, generation of large-scale summarization datasets in order of couple of millions is yet to be explored. ...
Recent models in developing summarization systems consist of millions of parameters and the model performance is highly dependent on the abundance of training data. ...
Prevalence of social media platforms has provided communities with an opportunity to ex- change different types of data while interacting with each other. ...
arXiv:2110.01159v2
fatcat:mlhy4vz32zfi5fpycemzqycwwi
"TL;DR:" Out-of-Context Adversarial Text Summarization and Hashtag Recommendation
[article]
2021
arXiv
pre-print
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This paper presents Out-of-Context Summarizer, a tool that takes arbitrary public news articles out of context by summarizing them to coherently fit either a liberal- or conservative-leaning agenda. ...
Out-of-Context Summarizer achieved 79% precision and 99% recall when summarizing COVID-19 articles, 93% precision and 93% recall when summarizing politically-centered articles, and 87% precision and 88% ...
Figure 1 : 1 Out-of-Context Summarizer Dataflow diagram showing how data flows through the Out-of-Context Summarizer, from the original text in the external news site to the delivery mechanism. ...
arXiv:2104.00782v1
fatcat:o4gwzmmxsnh55bl2gv3ukuqkxu
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