ScholarlyRead: A New Dataset for Scientific Article Reading Comprehension (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing studies on MRC on scholarly articles have focused on general domain datasets of news articles and elementary school-level storybooks. |
| Approach: | They propose to generate automatic questions from span-of-word-based scholarly articles’ Reading Comprehension dataset with approximately 10K manually checked passage-question-answer instances. |
| Outcome: | The proposed model yields the F1 score of 37.31% and is useful for building Question-Answering (QA) systems on scientific articles. |
Similar Papers
BioRead: A New Dataset for Biomedical Reading Comprehension (L18-1)
Copied to clipboard
| Challenge: | BioRead is a publicly available cloze-style biomedical machine reading comprehension (MRC) dataset with 16.4 million passage-question instances. |
| Approach: | They propose to build a cloze-style biomedical machine reading comprehension (MRC) dataset with 16.4 million passage-question instances. |
| Outcome: | The proposed method outperforms baselines on bioReadLite and bioASQ, and is currently the best on BioReadLite. |
SciDQA: A Deep Reading Comprehension Dataset over Scientific Papers (2024.emnlp-main)
Copied to clipboard
| Challenge: | SciDQA is a dataset for question-answering that challenges language models to deeply understand scientific articles. |
| Approach: | They propose a new dataset for reading comprehension that challenges language models to deeply understand scientific articles consisting of 2,937 QA pairs. |
| Outcome: | The SciDQA dataset is based on 2,937 QA pairs and decontextualizes the content, tracks the source document across different versions, and incorporates a bibliography for multi-document question-answering. |
Datasets for Scientific Literature Understanding: A Survey (2026.findings-acl)
Copied to clipboard
| Challenge: | Empowering machines to understand scientific literature is crucial for accelerating scientific discovery and advancing the AI for Science paradigm. |
| Approach: | They propose a systematic taxonomy that organizes resources spanning structural understanding, text understanding, multimodal understanding and pre-training/instruction fine-tuning. |
| Outcome: | The proposed taxonomy organizes resources spanning structural understanding, text understanding, multimodal understanding and pre-training/instruction fine-tuning. |
SumPubMed: Summarization Dataset of PubMed Scientific Articles (2021.acl-srw)
Copied to clipboard
| Challenge: | Existing summarization models that can extract the top few lines of news articles fail to summarize long documents. |
| Approach: | They constructed a scientific summarization dataset from MEDLINE articles from the PubMed archive to address this problem. |
| Outcome: | The proposed model outperforms existing models on news article summarization datasets and shows that it is more efficient to extract the top few lines. |
ACL-rlg: A Dataset for Reading List Generation (2025.coling-main)
Copied to clipboard
| Challenge: | Existing tools for searching the literature return an overwhelming number of results, making familiarization process daunting and inefficient. |
| Approach: | They propose to use ACL-rlg as the largest open expert-annotated reading list dataset to help researchers navigate key literature. |
| Outcome: | The proposed dataset outperforms existing search engines and indexing methods and shows signs of data contamination. |
English Machine Reading Comprehension Datasets: A Survey (2021.emnlp-main)
Copied to clipboard
| Challenge: | a survey of English Machine Reading Comprehension datasets is carried out . the aim is to provide a concise yet informative overview of the landscape . |
| Approach: | They survey 60 English Machine Reading Comprehension datasets to provide a resource for other researchers interested in this problem. |
| Outcome: | The proposed survey covers 60 English MRC datasets with a view to providing a resource for other researchers interested in the problem. |
Automatic Generation of Citation Texts in Scholarly Papers: A Pilot Study (2020.acl-main)
Copied to clipboard
| Challenge: | Existing studies on automatic generation of citation texts in scholarly papers have not investigated this problem. |
| Approach: | They propose to train an implicit citation extraction model based on BERT and a multi-source pointer-generator network with cross attention mechanism for citation text generation. |
| Outcome: | The proposed model can generate short texts to describe cited papers in scholarly papers with training data. |
SciReviewGen: A Large-scale Dataset for Automatic Literature Review Generation (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing literature review models have addressed literature review generation, but lack of large-scale datasets has been a stumbling block. |
| Approach: | They propose to use a large-scale dataset to evaluate automatic literature review generation models. |
| Outcome: | The proposed model can generate summaries comparable to human-written reviews while lacking detailed information. |
SciMRC: Multi-perspective Scientific Machine Reading Comprehension (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing datasets focused on single-perspective question-answer pairs overlooking inherent variation in comprehension levels among different readers. |
| Approach: | They propose a multi-perspective scientific machine reading comprehension dataset . their dataset comprises 741 scientific papers and 6,057 question-answer pairs . |
| Outcome: | The proposed dataset includes questions from beginners, students, and experts. |
How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular Benchmarks (D18-1)
Copied to clipboard
| Challenge: | Recent research addresses reading comprehension, where examples consist of (question, passage, answer) tuples. |
| Approach: | They establish sensible baselines for bAbI, SQuAD, CBT, CNN and Who-did-What datasets and compare them to their previous work. |
| Outcome: | The proposed models perform on 14 out of 20 bAbI, SQuAD, CBT, CNN and Who-did-What datasets. |