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Paper Abstract Writing through Editing Mechanism

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arxiv 1805.06064 v1 pith:XOTQK2NH submitted 2018-05-15 cs.CL cs.AI

Qingyun Wang , Zhihao Zhou , Lifu Huang , Spencer Whitehead , Boliang Zhang , Heng Ji , Kevin Knight This is my paper · ORCID
classification cs.CLcs.AI
keywords abstractratesystemteststitleturingwritingabstracts
verification ladder T0 review T1 audit T2 compute T3 formal
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We present a paper abstract writing system based on an attentive neural sequence-to-sequence model that can take a title as input and automatically generate an abstract. We design a novel Writing-editing Network that can attend to both the title and the previously generated abstract drafts and then iteratively revise and polish the abstract. With two series of Turing tests, where the human judges are asked to distinguish the system-generated abstracts from human-written ones, our system passes Turing tests by junior domain experts at a rate up to 30% and by non-expert at a rate up to 80%.

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  1. Exploring LLM Capabilities in Extracting DCAT-Compatible Metadata for Data Cataloging

    cs.IR 2025-07 conditional novelty 5.0 of 10

    Large language models can generate DCAT-compatible metadata for data catalogs at quality close to human annotations, though the strongest evidence is for simple extraction tasks.

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