Grunnleggende konsepter
ACLSum introduces a novel dataset for multi-aspect summarization of scientific papers, addressing the limitations of existing resources.
Sammendrag
Extensive efforts have been made to develop summarization datasets, but many are automatically generated, leading to subpar resources.
ACLSum is a carefully crafted dataset for multi-aspect summarization of scientific papers, focusing on challenges, approaches, and outcomes.
The dataset enables evaluation of models based on pretrained language models and large language models.
ACLSum facilitates extractive versus abstractive summarization evaluation within the scholarly domain.
The dataset is manually annotated and validated by domain experts, providing gold standard annotations for aspects and summaries.
Experiments show the effectiveness of different summarization strategies using ACLSum.
The dataset is limited in size and focuses on English NLP papers from specific conferences.
Statistikk
"ACLSum facilitates multi-aspect summarization of scientific papers."
"ACLSum contains 250 documents with an average length of approximately 40 sentences and 1,000 words."
"Extractive models trained on gold labels outperform those trained on silver labels."
Sitater
"ACLSum facilitates multi-aspect summarization of scientific papers."
"Extractive models trained on gold labels outperform those trained on silver labels."