#text_summarization search results
learn how to combine #Reinforcement_learning with #deep_learning for abstractive #text_summarization bit.ly/2MDlUHC bit.ly/eazysum #artificial_intelligence #ai #machinelearning #deeplearning #nlp
Avail our #text_summarization service to turn your lengthy documents into crisp, concise summaries. You get the perfect summarization through a combination of Natural language processing & human expertise. Want to know more? Visit: bit.ly/3aHYqkV
This is just Amazing! youtu.be/ogrJaOIuBx4 #text_summarization #DeepLearning
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How to Make a Text Summarizer - Intro to Deep Learning #10
Arumae et al. #NAACL2019, #Text_Summarization Approach: Introducing an Extractive Summarization technique with Question-Answering rewards. It uses Reinforcement Learning to explore the space of possible summaries and assess each one using a reward function.
3rd Paper: Data-efficient Neural Text Compression with Interactive Learning By: Avinesh P.V.S and Christian M. Meyer #NAACL2019, #Text_Summarization
Session 6B 5th Paper: Guiding Extractive Summarization with Question-Answering Rewards By: Kristjan Arumae and Fei Liu #NAACL2019 , #Text_Summarization
2nd Paper: Automatic learner summary assessment for reading comprehension By: Menglin Xia, Ekaterina Kochmar, Ted Briscoe #NAACL2019 #Text_Summarization
Peng et al. #NAACL2019 #text_summarization Problem: Improving the performance of the Conditional Text Generation models to make them more controllable and reliable.
Xia et al. #NAACL2019 #Text_Summarization Approach: Automated summarization using : - Feature Extracted-based method - CNN-based method (sentence pair similarity matrix) - LSTM-based method
Session 6B. 4th Paper: Text Generation with Exemplar-based Adaptive Decoding By: Hao Peng, Ankur P. Parikh, Manaal Faruqui, Bhuwan Dhingra, Dipanjan Das #NAACL2019 , #Text_Summarization
Kim et al. #NAACL2019, #Text_Summarization Approach: 1- Using another dataset: Reddit TIFU dataset as an informal dataset 2- An abstractive text summarization called multi-level memory networks (MMN) to store the information of text from different levels of abstraction.
Avinesh et al. #NAACL2019 #text_summarization Problem: -Seq2Seq Text Compression methods need huge datasets that are only available for a few domains. - Lack of generalizability. So, how to prepare a huge source and compressed version datasets to train models in new domains.
Arumae et al. #NAACL2019 #text_summarization Problem: Development of a supervised extractive summarizer that can highlight the text is challenging due to the lack of ground-truth datasets. Looking for a silent and consecutive sequence of words in text to highlight.
Kim et al. #NAACL2019, #Text_Summarization Problem: Abstractive summarization models suffer from training by datasets prepared with formal documents (news) that have biases. Key sentences locate at the beginning of the text and summary candidates are already inside the text.
Live @NAACL2019. Session 6B. 1st Paper: Abstractive Summarization of Reddit Posts with Multi-level Memory Networks By: Byeongchang Kim, Hyunwoo Kim, Gunhee Kim #NAACL2019 #Text_Summarization
مقدمة لتلخيص النصوص – #Text_Summarization هذا المقال يعد مقدمة لتلخيص النصوص وسنتعرض لنظرة عامة للطرق المستخدمة. سنقوم بمقارنة طريقتين أساسيتين... jisrlabs.com/%D9%85%D9%82%D…
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Auto-Summarization Tool #TextTeaser Relaunches As Open Source Code: TextTeaser, the #text_summarization API that... dlvr.it/626q59
#TextTeaser Lets Developers Integrate #Text_Summarization Into Their Apps And Sites: TextTeaser is a service that... dlvr.it/45WKKG
Avail our #text_summarization service to turn your lengthy documents into crisp, concise summaries. You get the perfect summarization through a combination of Natural language processing & human expertise. Want to know more? Visit: bit.ly/3aHYqkV
learn how to combine #Reinforcement_learning with #deep_learning for abstractive #text_summarization bit.ly/2MDlUHC bit.ly/eazysum #artificial_intelligence #ai #machinelearning #deeplearning #nlp
Arumae et al. #NAACL2019, #Text_Summarization Approach: Introducing an Extractive Summarization technique with Question-Answering rewards. It uses Reinforcement Learning to explore the space of possible summaries and assess each one using a reward function.
Arumae et al. #NAACL2019 #text_summarization Problem: Development of a supervised extractive summarizer that can highlight the text is challenging due to the lack of ground-truth datasets. Looking for a silent and consecutive sequence of words in text to highlight.
Session 6B 5th Paper: Guiding Extractive Summarization with Question-Answering Rewards By: Kristjan Arumae and Fei Liu #NAACL2019 , #Text_Summarization
Peng et al. #NAACL2019 #text_summarization Problem: Improving the performance of the Conditional Text Generation models to make them more controllable and reliable.
Session 6B. 4th Paper: Text Generation with Exemplar-based Adaptive Decoding By: Hao Peng, Ankur P. Parikh, Manaal Faruqui, Bhuwan Dhingra, Dipanjan Das #NAACL2019 , #Text_Summarization
Avinesh et al. #NAACL2019 #text_summarization Problem: -Seq2Seq Text Compression methods need huge datasets that are only available for a few domains. - Lack of generalizability. So, how to prepare a huge source and compressed version datasets to train models in new domains.
3rd Paper: Data-efficient Neural Text Compression with Interactive Learning By: Avinesh P.V.S and Christian M. Meyer #NAACL2019, #Text_Summarization
Xia et al. #NAACL2019 #Text_Summarization Approach: Automated summarization using : - Feature Extracted-based method - CNN-based method (sentence pair similarity matrix) - LSTM-based method
2nd Paper: Automatic learner summary assessment for reading comprehension By: Menglin Xia, Ekaterina Kochmar, Ted Briscoe #NAACL2019 #Text_Summarization
Kim et al. #NAACL2019, #Text_Summarization Approach: 1- Using another dataset: Reddit TIFU dataset as an informal dataset 2- An abstractive text summarization called multi-level memory networks (MMN) to store the information of text from different levels of abstraction.
Kim et al. #NAACL2019, #Text_Summarization Problem: Abstractive summarization models suffer from training by datasets prepared with formal documents (news) that have biases. Key sentences locate at the beginning of the text and summary candidates are already inside the text.
Live @NAACL2019. Session 6B. 1st Paper: Abstractive Summarization of Reddit Posts with Multi-level Memory Networks By: Byeongchang Kim, Hyunwoo Kim, Gunhee Kim #NAACL2019 #Text_Summarization
مقدمة لتلخيص النصوص – #Text_Summarization هذا المقال يعد مقدمة لتلخيص النصوص وسنتعرض لنظرة عامة للطرق المستخدمة. سنقوم بمقارنة طريقتين أساسيتين... jisrlabs.com/%D9%85%D9%82%D…
This is just Amazing! youtu.be/ogrJaOIuBx4 #text_summarization #DeepLearning
youtube.com
YouTube
How to Make a Text Summarizer - Intro to Deep Learning #10
Auto-Summarization Tool #TextTeaser Relaunches As Open Source Code: TextTeaser, the #text_summarization API that... dlvr.it/626q59
#TextTeaser Lets Developers Integrate #Text_Summarization Into Their Apps And Sites: TextTeaser is a service that... dlvr.it/45WKKG
learn how to combine #Reinforcement_learning with #deep_learning for abstractive #text_summarization bit.ly/2MDlUHC bit.ly/eazysum #artificial_intelligence #ai #machinelearning #deeplearning #nlp
Avail our #text_summarization service to turn your lengthy documents into crisp, concise summaries. You get the perfect summarization through a combination of Natural language processing & human expertise. Want to know more? Visit: bit.ly/3aHYqkV
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