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pro vyhledávání: '"Pu, Dongqi"'
Autor:
Pu, Dongqi, Demberg, Vera
For long document summarization, discourse structure is important to discern the key content of the text and the differences in importance level between sentences. Unfortunately, the integration of rhetorical structure theory (RST) into parameter-eff
Externí odkaz:
http://arxiv.org/abs/2405.00657
Scientific news reports serve as a bridge, adeptly translating complex research articles into reports that resonate with the broader public. The automated generation of such narratives enhances the accessibility of scholarly insights. In this paper,
Externí odkaz:
http://arxiv.org/abs/2403.17768
Autor:
Pu, Dongqi, Demberg, Vera
Large-scale language models, like ChatGPT, have garnered significant media attention and stunned the public with their remarkable capacity for generating coherent text from short natural language prompts. In this paper, we aim to conduct a systematic
Externí odkaz:
http://arxiv.org/abs/2306.07799
For text summarization, the role of discourse structure is pivotal in discerning the core content of a text. Regrettably, prior studies on incorporating Rhetorical Structure Theory (RST) into transformer-based summarization models only consider the n
Externí odkaz:
http://arxiv.org/abs/2305.16784