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The "Fat Face" illusion: A robust adaptation for processing pairs of faces
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In: ISSN: 0042-6989 ; EISSN: 0042-6989 ; Vision Research ; https://hal.archives-ouvertes.fr/hal-03579276 ; Vision Research, Elsevier, 2022, 195, pp.108015. ⟨10.1016/j.visres.2022.108015⟩ (2022)
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Speaking clearly improves speech segmentation by statistical learning under optimal listening conditions ...
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SLOGAN: Handwriting Style Synthesis for Arbitrary-Length and Out-of-Vocabulary Text ...
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Fostering student engagement with feedback: an integrated approach
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O desenho de uma aplicação de MAVL em PLE destinado a aprendentes chineses
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In: Entrepalavras; v. 11, n. 11esp (11): Dicionário, léxico e ensino de línguas; 313-339 (2022)
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Making Better Use of Bilingual Information for Cross-Lingual AMR Parsing ...
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Speaking clearly improves speech segmentation by statistical learning under optimal listening conditions
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In: Laboratory Phonology: Journal of the Association for Laboratory Phonology; Vol 12, No 1 (2021); 14 ; 1868-6354 (2021)
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Pushing Paraphrase Away from Original Sentence: A Multi-Round Paraphrase Generation Approach ...
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EarlyBERT: Efficient BERT Training via Early-bird Lottery Tickets ...
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Modeling Endorsement for Multi-Document Abstractive Summarization ...
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Abstract:
A crucial difference between single- and multi-document summarization is how salient content manifests itself in the document(s). While such content may appear at the beginning of a single document, essential information is frequently reiterated in a set of documents related to a particular topic, resulting in an endorsement effect that increases information salience. In this paper, we model the cross-document endorsement effect and its utilization in multiple document summarization. Our method generates a synopsis from each document, which serves as an endorser to identify salient content from other documents. Strongly endorsed text segments are used to enrich a neural encoder-decoder model to consolidate them into an abstractive summary. The method has a great potential to learn from fewer examples to identify salient content, which alleviates the need for costly retraining when the set of documents is dynamically adjusted. Through extensive experiments on benchmark multi-document summarization datasets, ...
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Keyword:
Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing; Text Summarization
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URL: https://dx.doi.org/10.48448/dpe6-2t70 https://underline.io/lecture/39832-modeling-endorsement-for-multi-document-abstractive-summarization
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Context-Aware Interaction Network for Question Matching ...
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Weakly Supervised Named Entity Tagging with Learnable Logical Rules ...
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Additional file 3 of Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models ...
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Additional file 3 of Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models ...
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Weakly Supervised Named Entity Tagging with Learnable Logical Rules ...
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Injecting Semantic Concepts into End-to-End Image Captioning ...
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Overcoming Barriers to Cross-cultural Cooperation in AI Ethics and Governance
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