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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 5 of 5 1 Plan-then-Generate: Controlled Data-to-Text Generation via Planning ... Su, Yixuan; Vandyke, David; Wang, Sihui. - : arXiv, 2021 BASE Show details 2 Plan-then-Generate: Controlled Data-to-Text Generation via Planning ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Nigel; Fang, Yimai. - : Underline Science Inc., 2021 BASE Show details 3 A Generative Model for Joint Natural Language Understanding and Generation ... Tseng, Bo-Hsiang; Cheng, Jianpeng; Fang, Yimai. - : arXiv, 2020 BASE Show details 4 Proposition-based summarization with a coherence-driven incremental model ... Fang, Yimai. - : Apollo - University of Cambridge Repository, 2019 Abstract: Summarization models which operate on meaning representations of documents have been neglected in the past, although they are a very promising and interesting class of methods for summarization and text understanding. In this thesis, I present one such summarizer, which uses the proposition as its meaning representation. My summarizer is an implementation of Kintsch and van Dijk's model of comprehension, which uses a tree of propositions to represent the working memory. The input document is processed incrementally in iterations. In each iteration, new propositions are connected to the tree under the principle of local coherence, and then a forgetting mechanism is applied so that only a few important propositions are retained in the tree for the next iteration. A summary can be generated using the propositions which are frequently retained. Originally, this model was only played through by hand by its inventors using human-created propositions. In this work, I turned it into a fully automatic model using ... Keyword: abstractive summarisation; abstractive summarization; coherence; computational linguistics; document understanding; natural language processing; NLP; summarisation; summarization; text understanding URL: https://www.repository.cam.ac.uk/handle/1810/287468 https://dx.doi.org/10.17863/cam.34773 BASE Hide details 5 Proposition-based summarization with a coherence-driven incremental model Fang, Yimai. - : University of Cambridge, 2018. : Computer Science and Technology, 2018. : Hughes Hall, 2018 BASE Show details

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