SlowGuess/ACL_2020_OCR huggingface dataset:
for Computational Linguistics: Human Language Technologies, volume 1, pages 2227-2237.
Daniele Pighin, Marco Cornolti, Enrique Alfonseca, and Katja Filippova. 2014. Modelling events through memory-based, open-ie patterns for abstractive summarization. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, volume 1, pages 892-901.
Dragomir Radev. 2000. A common theory of information fusion from multiple text sources step one: cross-document structure. In 1st SIGdial workshop on Discourse and dialogue.
Alec Radford, Karthik Narasimhan, Tim Salimans, and Ilya Sutskever. Improving language understanding by generative pre-training.
Sascha Rothe, Shashi Narayan, and Aliaksei Severyn. 2019. Leveraging pre-trained checkpoints for sequence generation tasks. arXiv preprint arXiv:1907.12461.
Abigail See, Peter J Liu, and Christopher D Manning. 2017. Get to the point: Summarization with pointer-generator networks. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pages 1073-1083.
Eva Sharma, Luyang Huang, Zhe Hu, and Lu Wang. 2019. An entity-driven framework for abstractive summarization. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, pages 3278-3289.
Yu Sun, Shuohuan Wang, Yukun Li, Shikun Feng, Hao Tian, Hua Wu, and Haifeng Wang. 2019. Ernie 2.0: A continual pre-training framework for language understanding. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence.
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. 2016. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2818-2826.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in neural information processing systems, pages 5998-6008.
Xiaojun Wan. 2008. An exploration of document impact on graph-based multi-document summarization. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 755-762. Association for Computational Linguistics.
Xiaojun Wan and Jianguo Xiao. 2009. Graph-based multi-modality learning for topic-focused multi-document summarization. In Proceedings of the
6242
21st International Joint Conference on Artificial Intelligence.
Lu Wang and Claire Cardie. 2013. Domain-independent abstract generation for focused meeting summarization. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), volume 1, pages 1395-1405.
Wenmian Yang, Weijia Jia, Wenyuan Gao, Xiaojie Zhou, and Yutao Luo. 2019a. Interactive variance attention based online spoiler detection for time-sync comments. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pages 1241-1250. ACM.
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, and Quoc V Le. 2019b. Xlnet: Generalized autoregressive pretraining for language understanding. In Advances in Neural Information Processing Systems (NIPS 2019), pages 5754-5764.
Michihiro Yasunaga, Rui Zhang, Kshitijh Meelu, Ayush Pareek, Krishnan Srinivasan, and Dragomir Radev. 2017. Graph-based neural multi-document summarization. In Proceedings of the 21st Conference on Computational Natural Language Learning, pages 452-462.
Yongjing Yin, Linfeng Song, Jinsong Su, Jiali Zeng, Chulun Zhou, and Jiebo Luo. 2019. Graph-based neural sentence ordering. In Proceedings of the 28th International Joint Conference on Artificial Intelligence, pages 5387-5393.
Jianmin Zhang, Jiwei Tan, and Xiaojun Wan. 2018. Towards a neural network approach to abstractive multi-document summarization. arXiv preprint arXiv:1804.09010.
We report the sentence-level ROUGE-L evaluation results of our models on both the two datasets, so that future work can compare with them conveniently.
Model	R-1	R-2	R-L
RoBERTa+FT	42.05	27.00	40.05
GraphSum	42.63	27.70	40.13
GraphSum+RoBERTa	42.99	27.83	40.97
Table 6: Evaluation results on the WikiSum test set with sentence-level ROUGE-L value.
Model	R-1	R-2	R-L
RoBERTa+FT	44.26	16.22	40.64
GraphSum	45.02	16.69	41.11
G.S.(Similarity)+RoBERTa	45.93	17.33	42.02
G.S.(Topic)+RoBERTa	46.07	17.42	42.22
G.S.(Discourse)+RoBERTa	45.87	17.56	42.00
Table 7: Evaluation results on the MultiNews test set with sentence-level ROUGE-L value.
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