Paper generation

Segmentation of robotic instruments is an important problem for robotic assisted minimially invasive surgery. It can be used for simple 2D applications such as overlay masking or 2D tracking but also for more complex 3D tasks such as pose estimation. In this challenge we invite applicants to participate in 3 different tasks: binary segmentation, multi-label segmentation and instrument recognition. Binary segmentation involves just separating the image into instruments and background, whereas multi-label segmentation requires the user to also recognize which parts of the instrument body correspond to the different articulated parts of a da Vinci robotic instrument. The final recogition task tests whether the user can recognize which segmentation corresponds to which da Vinci instrument type. To achieve this we are providing 8x 225-frame robotic surgical videos, captured at 2 Hz, where a trained team at Intuitive Surgical has manually labelled the different parts and types. The users are invited to test their algorithms on 8x 75-frame videos and 2x 300-frame videos which act as a test set. Description from: Robotic Instrument Segmentation Sub-Challenge Image source: https://endovissub2017-roboticinstrumentsegmentation.grand-challenge.org/

This dataset gathers 10,874 title and abstract pairs from the ACL Anthology Network (until 2016). The structure of the data is as follows: - title - abstract - \newline This dataset is used in our published paper: Paper Abstract Writing through Editing Mechanism ## Citation @inproceedings{wang-etal-2018-paper, title = "Paper Abstract Writing through Editing Mechanism", author = "Wang, Qingyun and Zhou, Zhihao and Huang, Lifu and Whitehead, Spencer and Zhang, Boliang and Ji, Heng and Knight, Kevin", booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)", month = jul, year = "2018", address = "Melbourne, Australia", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/P18-2042", doi = "10.18653/v1/P18-2042", pages = "260--265", abstract = "We present a paper abstract writing system based on an attentive neural sequence-to-sequence model that can take a title as input and automatically generate an abstract. We design a novel Writing-editing Network that can attend to both the title and the previously generated abstract drafts and then iteratively revise and polish the abstract. With two series of Turing tests, where the human judges are asked to distinguish the system-generated abstracts from human-written ones, our system passes Turing tests by junior domain experts at a rate up to 30{\%} and by non-expert at a rate up to 80{\%}.", }

A maintained database tracks ICLR submissions and reviews, augmented with author profiles and higher-level textual features.

This dataset gathers 14,857 entities, 133 relations, and entities corresponding tokenized text from PubMed. It contains 875,698 training pairs, 109,462 development pairs, and 109,462 test pairs.

This dataset gathers three types of pairs: Title-to-Abstract (Training: 22,811/Development: 2095/Test: 2095), Abstract-to-Conclusion and Future work (Training: 22,811/Development: 2095/Test: 2095), Conclusion and Future work-to-Title (Training: 15,902/Development: 2095/Test: 2095) from PubMed. Each pair contains a pair of input and output as well as the corresponding terms(from original KB and link prediction results).