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of 273
pro vyhledávání: '"GIPP, BELA"'
The quality of meeting summaries generated by natural language generation (NLG) systems is hard to measure automatically. Established metrics such as ROUGE and BERTScore have a relatively low correlation with human judgments and fail to capture nuanc
Externí odkaz:
http://arxiv.org/abs/2411.18444
Autor:
Horych, Tomas, Mandl, Christoph, Ruas, Terry, Greiner-Petter, Andre, Gipp, Bela, Aizawa, Akiko, Spinde, Timo
High annotation costs from hiring or crowdsourcing complicate the creation of large, high-quality datasets needed for training reliable text classifiers. Recent research suggests using Large Language Models (LLMs) to automate the annotation process,
Externí odkaz:
http://arxiv.org/abs/2411.11081
Meeting summarization is crucial in digital communication, but existing solutions struggle with salience identification to generate personalized, workable summaries, and context understanding to fully comprehend the meetings' content. Previous attemp
Externí odkaz:
http://arxiv.org/abs/2410.14545
Meeting summarization has become a critical task since digital encounters have become a common practice. Large language models (LLMs) show great potential in summarization, offering enhanced coherence and context understanding compared to traditional
Externí odkaz:
http://arxiv.org/abs/2407.11919
We present CiteAssist, a system to automate the generation of BibTeX entries for preprints, streamlining the process of bibliographic annotation. Our system extracts metadata, such as author names, titles, publication dates, and keywords, to create s
Externí odkaz:
http://arxiv.org/abs/2407.03192
Paraphrases represent a human's intuitive ability to understand expressions presented in various different ways. Current paraphrase evaluations of language models primarily use binary approaches, offering limited interpretability of specific text cha
Externí odkaz:
http://arxiv.org/abs/2407.02302
Publikováno v:
EMNLP 2024
Much of the success of modern language models depends on finding a suitable prompt to instruct the model. Until now, it has been largely unknown how variations in the linguistic expression of prompts affect these models. This study systematically and
Externí odkaz:
http://arxiv.org/abs/2406.19898
Abstractive dialogue summarization is the task of distilling conversations into informative and concise summaries. Although reviews have been conducted on this topic, there is a lack of comprehensive work detailing the challenges of dialogue summariz
Externí odkaz:
http://arxiv.org/abs/2406.07494
Text generation has become more accessible than ever, and the increasing interest in these systems, especially those using large language models, has spurred an increasing number of related publications. We provide a systematic literature review comp
Externí odkaz:
http://arxiv.org/abs/2405.15604
Meeting summarization has become a critical task considering the increase in online interactions. While new techniques are introduced regularly, their evaluation uses metrics not designed to capture meeting-specific errors, undermining effective eval
Externí odkaz:
http://arxiv.org/abs/2404.11124