3 papers
cs.CL2023
AraMUS: Pushing the Limits of Data and Model Scale for Arabic Natural Language Processing
Asaad Alghamdi, Xinyu Duan, Wei Jiang +9
Developing monolingual large Pre-trained Language Models (PLMs) is shown to be very successful in handling different tasks in Natural Language Processing (NLP). In this work, we pr…
cs.CL2023
Reference Matters: Benchmarking Factual Error Correction for Dialogue Summarization with Fine-grained Evaluation Framework
Mingqi Gao, Xiaojun Wan, Jia Su +2
Factuality is important to dialogue summarization. Factual error correction (FEC) of model-generated summaries is one way to improve factuality. Current FEC evaluation that relies…
cs.CL2023
CED: Catalog Extraction from Documents
Tong Zhu, Guoliang Zhang, Zechang Li +7
Sentence-by-sentence information extraction from long documents is an exhausting and error-prone task. As the indicator of document skeleton, catalogs naturally chunk documents int…