Siamese hierarchical attention networks for extractive summarization

[EN] In this paper, we present an extractive approach to document summarization based on Siamese Neural Networks. Specifically, we propose the use of Hierarchical Attention Networks to select the most relevant sentences of a text to make its summary. We train Siamese Neural Networks using document-summary pairs to determine whether the summary is appropriated for the document or not. By means of a sentence-level attention mechanism the most relevant sentences in the document can be identified. Hence, once the network is trained, it can be used to generate extractive summaries. The experimentation carried out using the CNN/DailyMail summarization corpus shows the adequacy of the proposal. In summary, we propose a novel end-to-end neural network to address extractive summarization as a binary classification problem which obtains promising results in-line with the state-of-the-art on the CNN/DailyMail corpus.This work has been partially supported by the Spanish MINECO and FEDER founds under project AMIC (TIN2017-85854-C4-2-R). Work of Jose-Angel Gonzalez is also financed by Universitat Politecnica de Valencia under grant PAID-01-17.González-Barba, JÁ.; Segarra Soriano, E.; García-Granada, F.; Sanchís Arnal, E.; Hurtado Oliver, LF. (2019). Siamese hierarchical attention networks for extractive summarization. Journal of Intelligent & Fuzzy Systems. 36(5):4599-4607. https://doi.org/10.3233/JIFS-179011S45994607365N. Begum , M. Fattah , and F. Ren . Automatic text summarization using support vector machine 5(7) (2009), 1987–1996.J. Cheng and M. Lapata . Neural summarization by extracting sentences and words. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016, August 7-12, 2016, Berlin, Germany, Volume 1: Long Papers, 2016.K.M. Hermann , T. Kocisky , E. Grefenstette , L. Espeholt , W. Kay , M. Suleyman , and P. Blunsom . Teaching machines to read and comprehend, CoRR, abs/1506.03340, 2015.D.P. Kingma and J. Ba . Adam: A method for stochastic optimization. CoRR, abs/1412.6980, 2014.Lloret, E., & Palomar, M. (2011). Text summarisation in progress: a literature review. Artificial Intelligence Review, 37(1), 1-41. doi:10.1007/s10462-011-9216-zLouis, A., & Nenkova, A. (2013). Automatically Assessing Machine Summary Content Without a Gold Standard. Computational Linguistics, 39(2), 267-300. doi:10.1162/coli_a_00123Miao, Y., & Blunsom, P. (2016). Language as a Latent Variable: Discrete Generative Models for Sentence Compression. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. doi:10.18653/v1/d16-1031R. Mihalcea and P. Tarau . Textrank: Bringing order into text. In Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, 2004.T. Mikolov , K. Chen , G. S. Corrado , and J. Dean . Efficient estimation of word representations in vector space, CoRR, abs/1301.3781, 2013.Minaee, S., & Liu, Z. (2017). Automatic question-answering using a deep similarity neural network. 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP). doi:10.1109/globalsip.2017.8309095R. Paulus , C. Xiong , and R. Socher , A deep reinforced model for abstractive summarization. CoRR, abs/1705.04304, 2017.Schuster, M., & Paliwal, K. K. (1997). Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing, 45(11), 2673-2681. doi:10.1109/78.650093See, A., Liu, P. J., & Manning, C. D. (2017). Get To The Point: Summarization with Pointer-Generator Networks. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). doi:10.18653/v1/p17-1099Takase, S., Suzuki, J., Okazaki, N., Hirao, T., & Nagata, M. (2016). Neural Headline Generation on Abstract Meaning Representation. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. doi:10.18653/v1/d16-1112G. Tur and R. De Mori . Spoken language understanding: Systems for extracting semantic information from speech, John Wiley & Sons, 2011

Neural Natural Language Inference Models Enhanced with External Knowledge

Modeling natural language inference is a very challenging task. With the availability of large annotated data, it has recently become feasible to train complex models such as neural-network-based inference models, which have shown to achieve the state-of-the-art performance. Although there exist relatively large annotated data, can machines learn all knowledge needed to perform natural language inference (NLI) from these data? If not, how can neural-network-based NLI models benefit from external knowledge and how to build NLI models to leverage it? In this paper, we enrich the state-of-the-art neural natural language inference models with external knowledge. We demonstrate that the proposed models improve neural NLI models to achieve the state-of-the-art performance on the SNLI and MultiNLI datasets.Comment: Accepted by ACL 201

Bias adjustment of infrared-based rainfall estimation using Passive Microwave satellite rainfall data

This study explores using Passive Microwave (PMW) rainfall estimation for spatial and temporal adjustment of Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System(PERSIANN-CCS). The PERSIANN-CCS algorithm collects information from infrared images to estimate rainfall. PERSIANN-CCS is one of the algorithms used in the IntegratedMultisatellite Retrievals for GPM (Global Precipitation Mission) estimation for the time period PMW rainfall estimations are limited or not available. Continued improvement of PERSIANN-CCS will support Integrated Multisatellite Retrievals for GPM for current as well as retrospective estimations of global precipitation. This study takes advantage of the high spatial and temporal resolution of GEO-based PERSIANN-CCS estimation and the more effective, but lower sample frequency, PMW estimation. The Probability Matching Method (PMM) was used to adjust the rainfall distribution of GEO-based PERSIANN-CCS toward that of PMW rainfall estimation. The results show that a significant improvement of global PERSIANN-CCS rainfall estimation is obtained