Datasets:
GEM
/
SciDuet

Tasks:
Other
Languages: English
Multilinguality: unknown
Size Categories: unknown
Language Creators: unknown
Annotations Creators: none
Source Datasets: original
License: apache-2.0
SciDuet / SciDuet.json
Sebastian Gehrmann
Data Card.
c33560c
{
"overview": {
"where": {
"has-leaderboard": "no",
"leaderboard-url": "N/A",
"leaderboard-description": "N/A",
"website": "[Huggingface](https://huggingface.co/datasets/GEM/SciDuet)",
"data-url": "[Github](https://github.com/IBM/document2slides/tree/main/SciDuet-ACL)",
"paper-url": "[ACL Anthology](https://aclanthology.org/2021.naacl-main.111/)",
"paper-bibtext": "```\n@inproceedings{sun-etal-2021-d2s,\n title = \"{D}2{S}: Document-to-Slide Generation Via Query-Based Text Summarization\",\n author = \"Sun, Edward and\n Hou, Yufang and\n Wang, Dakuo and\n Zhang, Yunfeng and\n Wang, Nancy X. R.\",\n booktitle = \"Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies\",\n month = jun,\n year = \"2021\",\n address = \"Online\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://aclanthology.org/2021.naacl-main.111\",\n doi = \"10.18653/v1/2021.naacl-main.111\",\n pages = \"1405--1418\",\n abstract = \"Presentations are critical for communication in all areas of our lives, yet the creation of slide decks is often tedious and time-consuming. There has been limited research aiming to automate the document-to-slides generation process and all face a critical challenge: no publicly available dataset for training and benchmarking. In this work, we first contribute a new dataset, SciDuet, consisting of pairs of papers and their corresponding slides decks from recent years{'} NLP and ML conferences (e.g., ACL). Secondly, we present D2S, a novel system that tackles the document-to-slides task with a two-step approach: 1) Use slide titles to retrieve relevant and engaging text, figures, and tables; 2) Summarize the retrieved context into bullet points with long-form question answering. Our evaluation suggests that long-form QA outperforms state-of-the-art summarization baselines on both automated ROUGE metrics and qualitative human evaluation.\",\n}\n```",
"contact-name": "N/A",
"contact-email": "N/A"
},
"languages": {
"is-multilingual": "no",
"license": "apache-2.0: Apache License 2.0",
"task-other": "N/A",
"language-names": [
"English"
],
"intended-use": "Promote research on the task of document-to-slides generation",
"license-other": "N/A",
"task": "Text-to-Slide"
},
"credit": {
"organization-type": [
"industry"
],
"organization-names": "IBM Research",
"creators": "Edward Sun, Yufang Hou, Dakuo Wang, Yunfeng Zhang, Nancy Wang",
"funding": "IBM Research",
"gem-added-by": "Yufang Hou (IBM Research), Dakuo Wang (IBM Research)"
},
"structure": {
"structure-labels": "The original papers and slides (both are in PDF format) are carefully processed by a combination of PDF/Image processing tookits. The text contents from multiple slides that correspond to the same slide title are mreged.",
"structure-splits": "Training, validation and testing data contain 136, 55, and 81 papers from ACL Anthology and their corresponding slides, respectively. ",
"structure-splits-criteria": "The dataset integrated into GEM is the ACL portion of the whole dataset described in the [paper](https://aclanthology.org/2021.naacl-main.111), It contains the full Dev and Test sets, and a portion of the Train dataset. \nNote that although we cannot release the whole training dataset due to copyright issues, researchers can still use our released data procurement code to generate the training dataset from the online ICML/NeurIPS anthologies."
},
"what": {
"dataset": "This dataset supports the document-to-slide generation task where a model has to generate presentation slide content from the text of a document. "
}
},
"curation": {
"original": {
"is-aggregated": "no",
"aggregated-sources": "N/A",
"rationale": "Provide a benchmark dataset for the document-to-slides task."
},
"language": {
"found": [],
"crowdsourced": [],
"created": "N/A",
"machine-generated": "N/A",
"validated": "not validated",
"is-filtered": "algorithmically",
"filtered-criteria": "the slide context text shouldn't contain additional format information such as \"*** University\" ",
"obtained": [
"Other"
],
"pre-processed": "Text on papers was extracted through Grobid. Figures andcaptions were extracted through pdffigures. Text on slides was extracted through IBM Watson Discovery package and OCR by pytesseract. Figures and tables that appear on slides and papers were linked through multiscale template matching by OpenCV. Further dataset\ncleaning was performed with standard string-based\nheuristics on sentence building, equation and floating caption removal, and duplicate line deletion."
},
"annotations": {
"origin": "none",
"rater-number": "N/A",
"rater-qualifications": "N/A",
"rater-training-num": "N/A",
"rater-test-num": "N/A",
"rater-annotation-service-bool": "no",
"rater-annotation-service": [],
"values": "N/A",
"quality-control": [],
"quality-control-details": "N/A"
},
"consent": {
"has-consent": "yes",
"consent-policy": "The original dataset was open-sourced under Apache-2.0. \nSome of the original dataset creators are part of the GEM v2 dataset infrastructure team and take care of integrating this dataset into GEM.",
"consent-other": "N/A",
"no-consent-justification": "N/A"
},
"pii": {
"has-pii": "yes/very likely",
"no-pii-justification": "N/A",
"is-pii-identified": "no identification",
"pii-identified-method": "N/A",
"is-pii-replaced": "N/A",
"pii-replaced-method": "N/A",
"pii-categories": [
"generic PII"
]
},
"maintenance": {
"has-maintenance": "no",
"description": "N/A",
"contact": "N/A",
"contestation-mechanism": "N/A",
"contestation-link": "N/A",
"contestation-description": "N/A"
}
},
"gem": {
"rationale": {
"sole-task-dataset": "no",
"distinction-description": "N/A",
"contribution": "SciDuet is the first publicaly available dataset for the challenging task of document2slides generation, which requires a model has a good ability to \"understand\" long-form text, choose appropriate content and generate key points.",
"model-ability": "content selection, long-form text undersanding and generation"
},
"curation": {
"has-additional-curation": "no",
"modification-types": [],
"modification-description": "N/A",
"has-additional-splits": "no",
"additional-splits-description": "N/A",
"additional-splits-capacicites": "N/A"
},
"starting": {}
},
"results": {
"results": {
"other-metrics-definitions": "N/A",
"has-previous-results": "yes",
"current-evaluation": "ROUGE + Human Evaluation",
"previous-results": "Paper \"D2S: Document-to-Slide Generation Via Query-Based\nText Summarization\" reports 20.47, 5.26 and 19.08 for ROUGE-1, ROUGE-2 and ROUGE-L (f-score).",
"model-abilities": "content selection, long-form text undersanding and key points generation",
"metrics": [
"ROUGE"
],
"original-evaluation": "Automatical Evaluation Metric: ROUGE\nHuman Evaluation: (Readability, Informativeness, Consistency) \n1) Readability: The generated slide content is coherent, concise, and grammatically correct;\n2) Informativeness: The generated slide provides sufficient and necessary information that corresponds to the given slide title, regardless of its similarity to the original slide;\n3) Consistency: The generated slide content is similar to the original author\u2019s reference slide."
}
},
"considerations": {
"pii": {},
"licenses": {
"dataset-restrictions-other": "N/A",
"data-copyright-other": "N/A",
"dataset-restrictions": [
"non-commercial use only"
],
"data-copyright": [
"research use only"
]
},
"limitations": {}
},
"context": {
"previous": {
"is-deployed": "no",
"described-risks": "N/A",
"changes-from-observation": "N/A"
},
"underserved": {
"helps-underserved": "no",
"underserved-description": "N/A"
},
"biases": {
"has-biases": "unsure",
"bias-analyses": "N/A"
}
}
}