Joint Graphical Models for Date Selection in Timeline Summarization

Tran, Giang Binh; Herder, Eelco; Markert, Katja

doi:10.3115/v1/p15-1154

Cited by 16 publications

(12 citation statements)

References 14 publications

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“…In addition, because of the strong context and semantic coherence between novel chapters, and the lack of the narrative coherence between the traditional multi-documents, it is difficult to apply the multi-document summarization techniques directly to summarize novel documents. For example, in (Tran et al, 2015), the authors used a joint graph model to carry out the multi-document summarization on the events which have occurred at different times, and finally obtained a good result. However, the simple event time series cannot deal with the complex plot lines in a novel.…”

Section: Multi-document Summarizationmentioning

confidence: 99%

A topic modeling based approach to novel document automatic summarization

et al. 2017

Expert Systems with Applications

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Most of existing text automatic summarization algorithms are targeted for multi-documents of relatively short length, thus difficult to be applied immediately to novel documents of structure freedom and long length. In this paper, aiming at novel documents, we propose a topic modeling based approach to extractive automatic summarization, so as to achieve a good balance among compression ratio, summarization quality and machine readability. First, based on topic modeling, we extract the candidate sentences associated with topic words from a preprocessed novel document. Second, with the goals of compression ratio and topic diversity, we design an importance evaluation function to select the most important sentences from the candidate sentences and thus generate an initial novel summary. Finally, we smooth the initial summary to overcome the semantic confusion caused by ambiguous or synonymous words, so as to improve the summary readability. We evaluate experimentally our proposed approach on a real novel dataset. The experiment results show that compared to those from other candidate algorithms, each automatic summary generated by our approach has not only a higher compression ratio, but also better summarization quality.

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Section: Multi-document Summarizationmentioning

confidence: 99%

A topic modeling based approach to novel document automatic summarization

et al. 2017

Expert Systems with Applications

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“…Wang et al (2016) tackle only the task of generating daily summaries without date selection using a supervised framework, greedily optimizing per-day predicted ROUGE scores, using images and text. In contrast, Kessler et al (2012) and Tran et al (2015b) only tackle date selection but do not generate any summaries. We consider the full task, including date selection and summary generation.…”

Section: Related Workmentioning

confidence: 99%

“…An important part of TLS is date selection. Dedicated algorithms for date selection use frequency and patterns in date referencing to determine date importance (Tran et al, 2015b). Most date importance measures can be integrated into the objective function to allow for joint date selection and summary generation.…”

Section: Date Selection Criteriamentioning

confidence: 99%

A Temporally Sensitive Submodularity Framework for Timeline Summarization

Martschat¹,

Markert²

2018

Proceedings of the 22nd Conference on Computational Natural Language Learning

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Timeline summarization (TLS) creates an overview of long-running events via dated daily summaries for the most important dates. TLS differs from standard multi-document summarization (MDS) in the importance of date selection, interdependencies between summaries of different dates and by having very short summaries compared to the number of corpus documents. However, we show that MDS optimization models using submodular functions can be adapted to yield wellperforming TLS models by designing objective functions and constraints that model the temporal dimension inherent in TLS. Importantly, these adaptations retain the elegance and advantages of the original MDS models (clear separation of features and inference, performance guarantees and scalability, little need for supervision) that current TLS-specific models lack. An open-source implementation of the framework and all models described in this paper is available online. 1 16 Predecessors of this task were the update and temporal summarization tasks (Aslam et al., 2015)

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“…In the context of the web, these techniques allow to go from unstructured or poorly structured text, to structured knowledge bases which lend themselves more easily to factchecking answers. Text analysis can also be used to detect trends in news, to extract the source of claims [24,35,34] or to recognize rumors [6]. There has also been some attempts to create end-to-end fact validation systems collecting and monitoring facts, from online data, and looking for evidences to input claims [27,22].…”

Section: Related Scientific Areasmentioning

confidence: 99%

Computational fact checking

Cazalens

Leblay²,

Lamarre

et al. 2018

Proc. VLDB Endow.

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Data journalism designates journalistic work inspired by digital data sources. A particularly popular and active area of data journalism is concerned with fact-checking. The term was born in the journalist community and referred the process of verifying and ensuring the accuracy of published media content; since 2012, however, it has increasingly focused on the analysis of politics, economy, science, and news content shared in any form, but first and foremost on the Web (social and otherwise). These trends have been noticed by computer scientists working in the industry and academia. Thus, a very lively area of digital content management research has taken up these problems and works to propose foundations (models), algorithms, and implement them through concrete tools. Our tutorial: (i) Outlines the current state of affairs in the area of digital (or computational) fact-checking in newsrooms, by journalists, NGO workers, scientists and IT companies; (ii) Shows which areas of digital content management research, in particular those relying on the Web, can be leveraged to help fact-checking, and gives a comprehensive survey of efforts in this area; (iii) Highlights ongoing trends, unsolved problems, and areas where we envision future scientific and practical advances.

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Joint Graphical Models for Date Selection in Timeline Summarization

Cited by 16 publications

References 14 publications

A topic modeling based approach to novel document automatic summarization

A topic modeling based approach to novel document automatic summarization

A Temporally Sensitive Submodularity Framework for Timeline Summarization

Computational fact checking

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