Datasets:
Tasks:
Summarization
Languages:
English
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Language Creators:
found
Annotations Creators:
no-annotation
Source Datasets:
original
ArXiv:
License:
| annotations_creators: | |
| - no-annotation | |
| language_creators: | |
| - found | |
| language: | |
| - en | |
| license: | |
| - unknown | |
| multilinguality: | |
| - monolingual | |
| size_categories: | |
| - 1K<n<10K | |
| source_datasets: | |
| - original | |
| task_categories: | |
| - summarization | |
| task_ids: [] | |
| paperswithcode_id: scitldr | |
| pretty_name: SciTLDR | |
| tags: | |
| - scientific-documents-summarization | |
| dataset_info: | |
| - config_name: Abstract | |
| features: | |
| - name: source | |
| sequence: string | |
| - name: source_labels | |
| sequence: | |
| class_label: | |
| names: | |
| '0': non-oracle | |
| '1': oracle | |
| - name: rouge_scores | |
| sequence: float32 | |
| - name: paper_id | |
| dtype: string | |
| - name: target | |
| sequence: string | |
| splits: | |
| - name: train | |
| num_bytes: 2738065 | |
| num_examples: 1992 | |
| - name: test | |
| num_bytes: 1073656 | |
| num_examples: 618 | |
| - name: validation | |
| num_bytes: 994876 | |
| num_examples: 619 | |
| download_size: 5483987 | |
| dataset_size: 4806597 | |
| - config_name: AIC | |
| features: | |
| - name: source | |
| sequence: string | |
| - name: source_labels | |
| sequence: | |
| class_label: | |
| names: | |
| '0': 0 | |
| '1': 1 | |
| - name: rouge_scores | |
| sequence: float32 | |
| - name: paper_id | |
| dtype: string | |
| - name: ic | |
| dtype: bool_ | |
| - name: target | |
| sequence: string | |
| splits: | |
| - name: train | |
| num_bytes: 14473822 | |
| num_examples: 1992 | |
| - name: test | |
| num_bytes: 4822026 | |
| num_examples: 618 | |
| - name: validation | |
| num_bytes: 4476237 | |
| num_examples: 619 | |
| download_size: 25545108 | |
| dataset_size: 23772085 | |
| - config_name: FullText | |
| features: | |
| - name: source | |
| sequence: string | |
| - name: source_labels | |
| sequence: | |
| class_label: | |
| names: | |
| '0': non-oracle | |
| '1': oracle | |
| - name: rouge_scores | |
| sequence: float32 | |
| - name: paper_id | |
| dtype: string | |
| - name: target | |
| sequence: string | |
| splits: | |
| - name: train | |
| num_bytes: 66917363 | |
| num_examples: 1992 | |
| - name: test | |
| num_bytes: 20182554 | |
| num_examples: 618 | |
| - name: validation | |
| num_bytes: 18790651 | |
| num_examples: 619 | |
| download_size: 110904552 | |
| dataset_size: 105890568 | |
| # Dataset Card for SciTLDR | |
| ## Table of Contents | |
| - [Dataset Description](#dataset-description) | |
| - [Dataset Summary](#dataset-summary) | |
| - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) | |
| - [Languages](#languages) | |
| - [Dataset Structure](#dataset-structure) | |
| - [Data Instances](#data-instances) | |
| - [Data Fields](#data-fields) | |
| - [Data Splits](#data-splits) | |
| - [Dataset Creation](#dataset-creation) | |
| - [Curation Rationale](#curation-rationale) | |
| - [Source Data](#source-data) | |
| - [Annotations](#annotations) | |
| - [Personal and Sensitive Information](#personal-and-sensitive-information) | |
| - [Considerations for Using the Data](#considerations-for-using-the-data) | |
| - [Social Impact of Dataset](#social-impact-of-dataset) | |
| - [Discussion of Biases](#discussion-of-biases) | |
| - [Other Known Limitations](#other-known-limitations) | |
| - [Additional Information](#additional-information) | |
| - [Dataset Curators](#dataset-curators) | |
| - [Licensing Information](#licensing-information) | |
| - [Citation Information](#citation-information) | |
| - [Contributions](#contributions) | |
| ## Dataset Description | |
| - **Homepage:** https://github.com/allenai/scitldr | |
| - **Repository:** https://github.com/allenai/scitldr | |
| - **Paper:** https://arxiv.org/abs/2004.15011 | |
| - **Leaderboard:** | |
| - **Point of Contact:** {isabelc,kylel,armanc,danw}@allenai.org | |
| ### Dataset Summary | |
| `SciTLDR`: Extreme Summarization of Scientific Documents | |
| SciTLDR is a new multi-target dataset of 5.4K TLDRs over 3.2K papers. SciTLDR contains both author-written and expert-derived TLDRs, where the latter are collected using a novel annotation protocol that produces high-quality summaries while minimizing annotation burden. | |
| ### Supported Tasks and Leaderboards | |
| summarization | |
| ### Languages | |
| English | |
| ## Dataset Structure | |
| SciTLDR is split in to a 60/20/20 train/dev/test split. For each file, each line is a json, formatted as follows | |
| ``` | |
| { | |
| "source":[ | |
| "sent0", | |
| "sent1", | |
| "sent2", | |
| ... | |
| ], | |
| "source_labels":[binary list in which 1 is the oracle sentence], | |
| "rouge_scores":[precomputed rouge-1 scores], | |
| "paper_id":"PAPER-ID", | |
| "target":[ | |
| "author-tldr", | |
| "pr-tldr0", | |
| "pr-tldr1", | |
| ... | |
| ], | |
| "title":"TITLE" | |
| } | |
| ``` | |
| The keys `rouge_scores` and `source_labels` are not necessary for any code to run, precomputed Rouge scores are provided for future research. | |
| ### Data Instances | |
| { | |
| "source": [ | |
| "Mixed precision training (MPT) is becoming a practical technique to improve the speed and energy efficiency of training deep neural networks by leveraging the fast hardware support for IEEE half-precision floating point that is available in existing GPUs.", | |
| "MPT is typically used in combination with a technique called loss scaling, that works by scaling up the loss value up before the start of backpropagation in order to minimize the impact of numerical underflow on training.", | |
| "Unfortunately, existing methods make this loss scale value a hyperparameter that needs to be tuned per-model, and a single scale cannot be adapted to different layers at different training stages.", | |
| "We introduce a loss scaling-based training method called adaptive loss scaling that makes MPT easier and more practical to use, by removing the need to tune a model-specific loss scale hyperparameter.", | |
| "We achieve this by introducing layer-wise loss scale values which are automatically computed during training to deal with underflow more effectively than existing methods.", | |
| "We present experimental results on a variety of networks and tasks that show our approach can shorten the time to convergence and improve accuracy, compared with using the existing state-of-the-art MPT and single-precision floating point." | |
| ], | |
| "source_labels": [ | |
| 0, | |
| 0, | |
| 0, | |
| 1, | |
| 0, | |
| 0 | |
| ], | |
| "rouge_scores": [ | |
| 0.2399999958000001, | |
| 0.26086956082230633, | |
| 0.19999999531250012, | |
| 0.38095237636054424, | |
| 0.2051282003944774, | |
| 0.2978723360796741 | |
| ], | |
| "paper_id": "rJlnfaNYvB", | |
| "target": [ | |
| "We devise adaptive loss scaling to improve mixed precision training that surpass the state-of-the-art results.", | |
| "Proposal for an adaptive loss scaling method during backpropagation for mix precision training where scale rate is decided automatically to reduce the underflow.", | |
| "The authors propose a method to train models in FP16 precision that adopts a more elaborate way to minimize underflow in every layer simultaneously and automatically." | |
| ], | |
| "title": "Adaptive Loss Scaling for Mixed Precision Training" | |
| } | |
| ### Data Fields | |
| - `source`: The Abstract, Introduction and Conclusion (AIC) or Full text of the paper, with one sentence per line. | |
| - `source_labels`: Binary 0 or 1, 1 denotes the oracle sentence. | |
| - `rouge_scores`: Precomputed ROUGE baseline scores for each sentence. | |
| - `paper_id`: Arxiv Paper ID. | |
| - `target`: Multiple summaries for each sentence, one sentence per line. | |
| - `title`: Title of the paper. | |
| ### Data Splits | |
| | | train | valid | test | | |
| |-------------------|-------|--------|------| | |
| | SciTLDR-A | 1992 | 618 | 619 | | |
| | SciTLDR-AIC | 1992 | 618 | 619 | | |
| | SciTLDR-FullText | 1992 | 618 | 619 | | |
| ## Dataset Creation | |
| [More Information Needed] | |
| ### Curation Rationale | |
| [More Information Needed] | |
| ### Source Data | |
| #### Initial Data Collection and Normalization | |
| [More Information Needed] | |
| #### Who are the source language producers? | |
| https://allenai.org/ | |
| ### Annotations | |
| #### Annotation process | |
| Given the title and first 128 words of a reviewer comment about a paper, | |
| re-write the summary (if it exists) into a single sentence or an incomplete | |
| phrase. Summaries must be no more than one sentence. | |
| Most summaries are between 15 and 25 words. The average rewritten summary is | |
| 20 words long. | |
| #### Who are the annotators? | |
| [More Information Needed] | |
| ### Personal and Sensitive Information | |
| [More Information Needed] | |
| ## Considerations for Using the Data | |
| ### Social Impact of Dataset | |
| To encourage further research in the area of extreme summarization of scientific documents. | |
| ### Discussion of Biases | |
| [More Information Needed] | |
| ### Other Known Limitations | |
| [More Information Needed] | |
| ## Additional Information | |
| ### Dataset Curators | |
| [More Information Needed] | |
| ### Licensing Information | |
| Apache License 2.0 | |
| ### Citation Information | |
| @article{cachola2020tldr, | |
| title={{TLDR}: Extreme Summarization of Scientific Documents}, | |
| author={Isabel Cachola and Kyle Lo and Arman Cohan and Daniel S. Weld}, | |
| journal={arXiv:2004.15011}, | |
| year={2020}, | |
| } | |
| ### Contributions | |
| Thanks to [@Bharat123rox](https://github.com/Bharat123rox) for adding this dataset. |