{"type":"ScholarlyArticle","title":"MMVQA: A Comprehensive Dataset for Investigating Multipage Multimodal Information Retrieval in PDF-based Visual Question Answering","authors":[{"name":"Yihao Ding","slug":"yihao-ding","given":"Yihao","family":"Ding","affiliation":"The University of Melbourne; The University of Sydney"},{"name":"Kaixuan Ren","slug":"kaixuan-ren","given":"Kaixuan","family":"Ren","affiliation":"The University of Sydney"},{"name":"Jiabin Huang","slug":"jiabin-huang","given":"Jiabin","family":"Huang","affiliation":"The University of Sydney"},{"name":"Siwen Luo","slug":"siwen-luo","given":"Siwen","family":"Luo","affiliation":"The University of Western Australia"},{"name":"Soyeon Caren Han","slug":"soyeon-caren-han","given":"Soyeon Caren","family":"Han","affiliation":"The University of Melbourne; The University of Sydney"}],"abstract":"Document Question Answering (QA) presents a challenge in understanding visually-rich documents (VRD), particularly with lengthy textual content. Existing studies primarily focus on real-world documents with sparse text, while challenges persist in comprehending the hierarchical semantic relations among multiple pages to locate multimodal components. The paper introduces PDF-MVQA, tailored for research journal articles, encompassing multiple pages and multimodal retrieval. Our approach aims to retrieve entire paragraphs containing answers or visually rich document entities like tables and figures. The main contribution is introducing a comprehensive PDF Document VQA dataset, allowing the examination of semantically hierarchical layout structures in text-dominant documents. We also present new VRD-QA frameworks to grasp textual contents and relations among document layouts simultaneously, extending page-level understanding to the entire multi-page document. We aim to enhance the capabilities of existing vision-and-language models in handling challenges posed by text-dominant documents in VRD-QA. Code and Appendix are in https://github.com/adlnlp/pdfmvqa","doi":"10.24963/ijcai.2024/690","url":"https://ijcai.org/proceedings/2024/690","pdf":"https://ijcai.org/proceedings/2024/0690.pdf","markdown":"https://ijcai.org/proceedings/2024/690.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/690","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Natural Language Processing","track":"Main Track","number":690,"pages":{"first":6243,"last":6251},"keywords":["Natural Language Processing: NLP: Information extraction","Natural Language Processing: NLP: Applications","Natural Language Processing: NLP: Resources and evaluation"],"videos":[],"references":[{"position":1,"raw":"[Ding et al., 2023a] Yihao Ding, Siqu Long, Jiabin Huang, Kaixuan Ren, Xingxiang Luo, Hyunsuk Chung, and Soyeon Caren Han. Form-nlu: Dataset for the form natural language understanding. In Proceedings of the 46th Inter- national ACM SIGIR Conference on Research and Devel- opment in Information Retrieval, pages 2807-2816, 2023.","title":"Form-nlu: Dataset for the form natural language understanding","authors":"Ding","year":"2023","venue":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Ding et al., 2023b] Yihao Ding, Siwen Luo, Hyunsuk Chung, and Soyeon Caren Han. Pdf-vqa: A new dataset for real-world vqa on pdf documents. In Gianmarco De Francisci Morales, Claudia Perlich, Natali Ruchansky, Nicolas Kourtellis, Elena Baralis, and Francesco Bonchi, editors, Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track, pages 585-601, Cham, 2023. Springer Nature Switzerland.","title":"Pdf-vqa: A new dataset for real-world vqa on pdf documents","authors":"Ding","year":"2023","venue":"Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Ding et al., 2024] Yihao Ding, Lorenzo Vaiani, Caren Han, Jean Lee, Paolo Garza, Josiah Poon, and Luca Cagliero. M3-vrd: Multimodal multi-task multi-teacher visually- rich form document understanding. arXiv preprint arXiv:2402.17983, 2024.","title":"M3-vrd: Multimodal multi-task multi-teacher visuallyrich form document understanding","authors":"Yihao Ding, Lorenzo Vaiani, Caren Han, Jean Lee, Paolo Garza, Josiah Poon, Luca Cagliero","year":"2024","venue":null,"doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Du et al., 2022] Qinyi Du, Qingqing Wang, Keqian Li, Ji- dong Tian, Liqiang Xiao, and Yaohui Jin. Calm: Commen- sense knowledge augmentation for document image un- derstanding. In Proceedings of the 30th ACM International Conference on Multimedia, pages 3282-3290, 2022.","title":"Calm: Commensense knowledge augmentation for document image understanding","authors":"Du","year":"2022","venue":"Proceedings of the 30th ACM International Conference on Multimedia","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Gu et al., 2021] Jiuxiang Gu, Jason Kuen, Vlad I Morariu, Handong Zhao, Rajiv Jain, Nikolaos Barmpalios, Ani Nenkova, and Tong Sun. Unidoc: Unified pretraining framework for document understanding. Advances in Neu- ral Information Processing Systems, 34:39-50, 2021.","title":"Unidoc: Unified pretraining framework for document understanding","authors":"Gu","year":"2021","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Hofstätter et al., 2023] Sebastian Hofstätter, Jiecao Chen, Karthik Raman, and Hamed Zamani. Fid-light: Efficient and effective retrieval-augmented text generation. In Pro- ceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 1437-1447, 2023.","title":"Fid-light: Efficient and effective retrieval-augmented text generation","authors":"Sebastian Hofstätter, Jiecao Chen, Karthik Raman, Hamed Zamani","year":"2023","venue":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Hu et al., 2023] Ziniu Hu, Ahmet Iscen, Chen Sun, Zirui Wang, Kai-Wei Chang, Yizhou Sun, Cordelia Schmid, David A Ross, and Alireza Fathi. Reveal: Retrieval- augmented visual-language pre-training with multi-source multimodal knowledge memory. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 23369-23379, 2023.","title":"Reveal: Retrievalaugmented visual-language pre-training with multi-source multimodal knowledge memory","authors":"Hu","year":"2023","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Huang et al., 2022] Yupan Huang, Tengchao Lv, Lei Cui, Yutong Lu, and Furu Wei. Layoutlmv3: Pre-training for document ai with unified text and image masking. In Pro- ceedings of the 30th ACM International Conference on Multimedia, pages 4083-4091, 2022.","title":"Layoutlmv3: Pre-training for document ai with unified text and image masking","authors":"Huang","year":"2022","venue":"Proceedings of the 30th ACM International Conference on Multimedia","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Kim et al., 2021] Wonjae Kim, Bokyung Son, and Ildoo Kim. Vilt: Vision-and-language transformer without con- volution or region supervision. In International Confer- ence on Machine Learning, pages 5583-5594. PMLR, 2021.","title":"Vilt: Vision-and-language transformer without convolution or region supervision","authors":"Kim","year":"2021","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Kim et al., 2022] Geewook Kim, Teakgyu Hong, Moonbin Yim, JeongYeon Nam, Jinyoung Park, Jinyeong Yim, Wonseok Hwang, Sangdoo Yun, Dongyoon Han, and Se- unghyun Park. Ocr-free document understanding trans- former. In European Conference on Computer Vision, pages 498-517. Springer, 2022.","title":"Ocr-free document understanding transformer","authors":"Kim","year":"2022","venue":"European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Li et al., 2019] Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, and Kai-Wei Chang. Visualbert: A simple and performant baseline for vision and language. arXiv preprint arXiv:1908.03557, 2019.","title":"Visualbert: A simple and performant baseline for vision and language","authors":"Li","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Li et al., 2021] Peizhao Li, Jiuxiang Gu, Jason Kuen, Vlad I Morariu, Handong Zhao, Rajiv Jain, Varun Manjunatha, and Hongfu Liu. Selfdoc: Self-supervised document rep- resentation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5652-5660, 2021.","title":"Selfdoc: Self-supervised document representation learning","authors":"Peizhao Li, Jiuxiang Gu, Jason Kuen, Handong Vlad I Morariu, Rajiv Zhao, Varun Jain, Hongfu Manjunatha, Liu","year":"2021","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Liu et al., 2023a] Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. Visual instruction tuning, 2023.","title":"Visual instruction tuning","authors":"Liu","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Liu et al., 2023b] Xuejing Liu, Wei Tang, Xinzhe Ni, Jinghui Lu, Rui Zhao, Zechao Li, and Fei Tan. What large language models bring to text-rich vqa? arXiv preprint arXiv:2311.07306, 2023.","title":"What large language models bring to text-rich vqa","authors":"Liu","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Long et al., 2022] Siqu Long, Feiqi Cao, Soyeon Caren Han, and Haiqin Yang. Vision-and-language pretrained models: A survey. In Lud De Raedt, editor, Proceedings of the Thirty-First International Joint Conference on Ar- tificial Intelligence, IJCAI-22, pages 5530-5537. Interna- tional Joint Conferences on Artificial Intelligence Organi- zation, 7 2022. Survey Track.","title":"Vision-and-language pretrained models: A survey","authors":"Long","year":"2022","venue":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22","doi":null,"url":null,"internal_url":"/proceedings/2022/773"},{"position":16,"raw":"[Mathew et al., 2021a] Minesh Mathew, Dimosthenis Karatzas, and CV Jawahar. Docvqa: A dataset for vqa on document images. In Proceedings of the IEEE/CVF winter conference on applications of computer vision, pages 2200-2209, 2021.","title":"Docvqa: A dataset for vqa on document images","authors":"Mathew","year":"2021","venue":"Proceedings of the IEEE/CVF winter conference on applications of computer vision","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Mathew et al., 2021b] Minesh Mathew, Dimosthenis Karatzas, and CV Jawahar. Docvqa: A dataset for vqa on document images. In Proceedings of the IEEE/CVF winter conference on applications of computer vision, pages 2200-2209, 2021.","title":"Docvqa: A dataset for vqa on document images","authors":"Mathew","year":"2021","venue":"Proceedings of the IEEE/CVF winter conference on applications of computer vision","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[McHugh, 2012] Mary L McHugh. Interrater reliability: the kappa statistic. Biochemia medica, 22(3):276-282, 2012.","title":"Interrater reliability: the kappa statistic","authors":"Mary L Mchugh","year":"2012","venue":"Biochemia medica","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Ouyang et al., 2022] Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. Training language models to follow instruc- tions with human feedback. Advances in Neural Informa- tion Processing Systems, 35:27730-27744, 2022.","title":"Training language models to follow instructions with human feedback","authors":"Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray","year":"2022","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Radford et al., 2021] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agar- wal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from nat- ural language supervision. In International conference on machine learning, pages 8748-8763. PMLR, 2021.","title":"Learning transferable visual models from natural language supervision","authors":"Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark","year":"2021","venue":"International conference on machine learning","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Tan and Bansal, 2019] Hao Tan and Mohit Bansal. Lxmert: Learning cross-modality encoder representations from transformers. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Lan- guage Processing (EMNLP-IJCNLP), pages 5100-5111, 2019.","title":"Lxmert: Learning cross-modality encoder representations from transformers","authors":"Bansal ; ] Tan, Tan Hao, Mohit Bansal","year":"2019","venue":"Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Tanaka et al., 2021] Ryota Tanaka, Kyosuke Nishida, and Sen Yoshida. Visualmrc: Machine reading comprehension on document images. In Proceedings of the AAAI Confer- ence on Artificial Intelligence, volume 35, pages 13878- 13888, 2021.","title":"Visualmrc: Machine reading comprehension on document images","authors":"Ryota Tanaka, Kyosuke Nishida, Sen Yoshida","year":"2021","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Tanaka et al., 2023] Ryota Tanaka, Kyosuke Nishida, Ko- suke Nishida, Taku Hasegawa, Itsumi Saito, and Kuniko Saito. Slidevqa: A dataset for document visual question answering on multiple images. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 37, pages 13636-13645, 2023.","title":"Slidevqa: A dataset for document visual question answering on multiple images","authors":"Ryota Tanaka, Kyosuke Nishida, Kosuke Nishida, Taku Hasegawa, Itsumi Saito, Kuniko Saito","year":"2023","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Tito et al., 2021] Rubèn Tito, Dimosthenis Karatzas, and Ernest Valveny. Document collection visual question an- swering. In Document Analysis and Recognition-ICDAR 2021: 16th International Conference, Lausanne, Switzer- land, September 5-10, 2021, Proceedings, Part II 16, pages 778-792. Springer, 2021.","title":"Rubèn Tito, Dimosthenis Karatzas, and Ernest Valveny. Document collection visual question answering","authors":"Tito","year":"2021","venue":"Document Analysis and Recognition-ICDAR 2021: 16th International Conference","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Tito et al., 2023] Rubèn Tito, Dimosthenis Karatzas, and Ernest Valveny. Hierarchical multimodal transformers for multipage docvqa. Pattern Recognition, 144:109834, 2023.","title":"Rubèn Tito, Dimosthenis Karatzas, and Ernest Valveny. Hierarchical multimodal transformers for multipage docvqa","authors":"Tito","year":"2023","venue":"Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Touvron et al., 2023] Hugo Touvron, Thibaut Lavril, Gau- tier Izacard, Xavier Martinet, Marie-Anne Lachaux, Tim- othée Lacroix, Baptiste Rozière, Naman Goyal, Eric Ham- bro, Faisal Azhar, et al. Llama: Open and efficient founda- tion language models. arXiv preprint arXiv:2302.13971, 2023.","title":"Hugo Touvron, Thibaut Lavril, Gautier Izacard","authors":"Touvron","year":"2023","venue":"Llama: Open and efficient foundation language models","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Wang et al., 2022] Jiapeng Wang, Lianwen Jin, and Kai Ding. Lilt: A simple yet effective language-independent layout transformer for structured document understanding. In Proceedings of the 60th Annual Meeting of the Associ- ation for Computational Linguistics (Volume 1: Long Pa- pers), pages 7747-7757, 2022.","title":"Lilt: A simple yet effective language-independent layout transformer for structured document understanding","authors":"Wang","year":"2022","venue":"Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Wu et al., 2022] Xinya Wu, Duo Zheng, Ruonan Wang, Ji- ashen Sun, Minzhen Hu, Fangxiang Feng, Xiaojie Wang, Huixing Jiang, and Fan Yang. A region-based document vqa. In Proceedings of the 30th ACM International Con- ference on Multimedia, pages 4909-4920, 2022.","title":"A region-based document vqa","authors":"Xinya Wu, Duo Zheng, Ruonan Wang, Jiashen Sun, Minzhen Hu, Fangxiang Feng, Xiaojie Wang, Huixing Jiang, Fan Yang","year":"2022","venue":"Proceedings of the 30th ACM International Conference on Multimedia","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Xu et al., 2021] Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Floren- cio, Cha Zhang, Wanxiang Che, et al. Layoutlmv2: Multi- modal pre-training for visually-rich document understand- ing. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Pro- cessing (Volume 1: Long Papers), pages 2579-2591, 2021.","title":"Layoutlmv2: Multimodal pre-training for visually-rich document understanding","authors":"Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che","year":"2021","venue":"Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Xu et al., 2023] Xiao Xu, Chenfei Wu, Shachar Rosenman, Vasudev Lal, Wanxiang Che, and Nan Duan. Bridgetower: Building bridges between encoders in vision-language representation learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 37, pages 10637-10647, 2023.","title":"Bridgetower: Building bridges between encoders in vision-language representation learning","authors":"Xiao Xu, Chenfei Wu, Shachar Rosenman, Vasudev Lal, Wanxiang Che, Nan Duan","year":"2023","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Yasunaga et al., 2022] Michihiro Yasunaga, Armen Agha- janyan, Weijia Shi, Rich James, Jure Leskovec, Percy Liang, Mike Lewis, Luke Zettlemoyer, and Wen-tau Yih. Retrieval-augmented multimodal language modeling. arXiv preprint arXiv:2211.12561, 2022.","title":"Retrieval-augmented multimodal language modeling","authors":"Michihiro Yasunaga, Armen Aghajanyan, Weijia Shi, Rich James, Jure Leskovec, Percy Liang, Mike Lewis, Luke Zettlemoyer, Wen-Tau Yih","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Ye et al., 2023] Yunhu Ye, Binyuan Hui, Min Yang, Binhua Li, Fei Huang, and Yongbin Li. Large language mod- els are versatile decomposers: Decomposing evidence and questions for table-based reasoning. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 174- 184, 2023.","title":"Large language models are versatile decomposers: Decomposing evidence and questions for table-based reasoning","authors":"Ye","year":"2023","venue":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Yu et al., 2022] Yuechen Yu, Yulin Li, Chengquan Zhang, Xiaoqiang Zhang, Zengyuan Guo, Xiameng Qin, Kun Yao, Junyu Han, Errui Ding, and Jingdong Wang. Struc- textv2: Masked visual-textual prediction for document im- age pre-training. In The Eleventh International Conference on Learning Representations, 2022.","title":"Structextv2: Masked visual-textual prediction for document image pre-training","authors":"Yuechen Yu, Yulin Li, Chengquan Zhang, Xiaoqiang Zhang, Zengyuan Guo, Xiameng Qin, Kun Yao, Junyu Han, Errui Ding, Jingdong Wang","year":"2022","venue":"The Eleventh International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Zaheer et al., 2020] Manzil Zaheer, Guru Guruganesh, Ku- mar Avinava Dubey, Joshua Ainslie, Chris Alberti, Santi- ago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, et al. Big bird: Transformers for longer se- quences. Advances in neural information processing sys- tems, 33:17283-17297, 2020.","title":"Big bird: Transformers for longer sequences","authors":"Manzil Zaheer, Guru Guruganesh, Avinava Kumar, Joshua Dubey, Chris Ainslie, Santiago Alberti, Philip Ontanon, Anirudh Pham, Qifan Ravula, Li Wang, Yang","year":"2020","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Zhao et al., 2023] Ruochen Zhao, Hailin Chen, Weishi Wang, Fangkai Jiao, Xuan Long Do, Chengwei Qin, Bosheng Ding, Xiaobao Guo, Minzhi Li, Xingxuan Li, et al. Retrieving multimodal information for augmented generation: A survey. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 4736- 4756, 2023.","title":"Retrieving multimodal information for augmented generation: A survey","authors":"Zhao","year":"2023","venue":"Findings of the Association for Computational Linguistics: EMNLP 2023","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Zhong et al., 2019] Xu Zhong, Jianbin Tang, and Anto- nio Jimeno Yepes. Publaynet: largest dataset ever for doc- ument layout analysis. In 2019 International Conference on Document Analysis and Recognition (ICDAR), pages 1015-1022. IEEE, 2019.","title":"Publaynet: largest dataset ever for document layout analysis","authors":"Xu Zhong, Jianbin Tang, Antonio Jimeno, Yepes","year":"2019","venue":"2019 International Conference on Document Analysis and Recognition (ICDAR)","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}