Coherent narrative summarization with a cognitive model

Zhang, Renxian; Li, Wenjie; Liu, Naishi; Gao, Dehong

doi:10.1016/j.csl.2015.07.004

Cited by 10 publications

(11 citation statements)

References 28 publications

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“…The application of cognitive theories of reading comprehension in summarization tasks has re-ceived increased attention in the last few years (Zhang et al, 2016;Lloret, 2012). Early work by Fang and Teufel (2014) introduced an effective computational implementation of the KvD theory and proposed an extractive summarizer based on greedy sentence ranking.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised Extractive Summarization by Human Memory Simulation

Cardenas,

Galle,

Cohen

2021

Preprint

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Summarization systems face the core challenge of identifying and selecting important information. In this paper, we tackle the problem of content selection in unsupervised extractive summarization of long, structured documents. We introduce a wide range of heuristics that leverage cognitive representations of content units and how these are retained or forgotten in human memory. We find that properties of these representations of human memory can be exploited to capture relevance of content units in scientific articles. Experiments show that our proposed heuristics are effective at leveraging cognitive structures and the organization of the document (i.e. sections of an article), and automatic and human evaluations provide strong evidence that these heuristics extract more summary-worthy content units.

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Section: Related Workmentioning

confidence: 99%

Unsupervised Extractive Summarization by Human Memory Simulation

Cardenas,

Galle,

Cohen

2021

Preprint

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“…To make text understandable, readers must establish coherent relations between informative parts of text [ZLLG16]. This is significantly important in a summary, where we usually want to condense informative portions, preserving the overall level of text comprehension.…”

Section: How Humans Summarize Textsmentioning

confidence: 99%

Automatic summary of texts in Polish

Ozimek¹

2020

Preprint

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The automatic text summarizing task is one of the most complex problems in the field of natural language processing. In this dissertation, we present the abstraction-based summarization approach which allows to paraphrase the original text and generate new sentences. Creation of new formulations, completely different from the original text is similar to how humans summarize texts. To achieve this, we propose the deep learning method using Sequence to Sequence architecture with the attention mechanism. The goal is to create the model for Polish language, using dataset containing over 200,000 articles from Polish websites, split into text and summary parts. Presented outcomes look promising, obtaining decent results utilizing standard metrics for such type of task.Based on review of prior research done during experiments, this is the very first attempt of applying abstractive text summarization techniques for Polish language.

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“…Additionally, macro-propositions depend on domain-specific schema, whereas our system aims to be domain-independent. Zhang et al (2016) presented a summariser based on a later cognitive model by Kintsch (1998). Instead of modelling importance of propositions directly, their summariser computes the importance of words by spreading activation cyclically, but extracts at proposition level.…”

Section: The Kvd Modelmentioning

confidence: 99%

“…NLI is in the core of natural language understanding and has wide applications in NLP, e.g., question answering (Harabagiu and Hickl, 2006) and automatic summarization (Lacatusu et al, 2006;Yan et al, 2011a;Yan et al, 2011b). Moreover, NLI is also related to other tasks of sentence pair modeling, including paraphrase detection (Hu et al, 2014), relation recognition of discourse units (Liu et al, 2016), etc.…”

Section: Introductionmentioning

confidence: 99%

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Gurevych

Miyao

2016

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Coherent narrative summarization with a cognitive model

Cited by 10 publications

References 28 publications

Unsupervised Extractive Summarization by Human Memory Simulation

Unsupervised Extractive Summarization by Human Memory Simulation

Automatic summary of texts in Polish

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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