[PDF] Top 20 Abstractive Sentence Summarization with Attentive Recurrent Neural Networks
Has 10000 "Abstractive Sentence Summarization with Attentive Recurrent Neural Networks" found on our website. Below are the top 20 most common "Abstractive Sentence Summarization with Attentive Recurrent Neural Networks".
Abstractive Sentence Summarization with Attentive Recurrent Neural Networks
... For the sake of completeness we also compare our models to the recently proposed standard Neu- ral Machine Translation (NMT) systems. In par- ticular, we compare to a smaller re-implementation of the attentive ... See full document
6
Selective Encoding for Abstractive Sentence Summarization
... for abstractive sentence summariza- tion. It consists of a sentence encoder, a selective gate network, and an atten- tion equipped ...The sentence en- coder and decoder are built with recur- ... See full document
10
An Improved Attention Layer assisted Recurrent Convolutional Neural Network Model for Abstractive Text Summarization
... for abstractive text summarization in multiple document ...free) summarization. Our proposed model generated a new sentence with low word count; however it was highly relevant, concise and of ... See full document
11
A Pilot Study of Domain Adaptation Effect for Neural Abstractive Summarization
... text summarization research moves to- wards producing abstractive summmaries, which better emulates human summarization process and produces more concise summaries (Nenkova et ...ral networks ... See full document
7
Controlling Length in Abstractive Summarization Using a Convolutional Neural Network
... in abstractive summarization on single sentence summarization, and it is much faster than the previous recurrent models as it can be easily ... See full document
10
Decoupling Encoder and Decoder Networks for Abstractive Document Summarization
... The current state-of-art system for the task is based on an attentive encoder and a recurrent de- coder (Chopra et al., 2016), which is an extension of the methodology of Rush et al. (2015). The encoder and ... See full document
5
Modeling Local Dependence in Natural Language with Multi-Channel Recurrent Neural Networks
... Recurrent Neural Networks (RNNs) have been widely used in processing natural language tasks and achieve huge suc- ...a sentence uniformly and ...a sentence, which is useful for ... See full document
8
Semantic Sentence Matching with Densely-Connected Recurrent and Co-Attentive Information
... deeper recurrent network for sentence matching like deep neural machine translator (NMT) (Wu et ...Deep recurrent models are more advantageous for learning long sequences and outperform the ... See full document
8
Fast Abstractive Summarization with Reinforce Selected Sentence Rewriting
... fast summarization model that first selects salient sentences and then rewrites them abstractively ...novel sentence-level pol- icy gradient method to bridge the non- differentiable computation between ... See full document
12
Entity Commonsense Representation for Neural Abstractive Summarization
... following abstractive baselines: ABS+ (Rush et ...an attentive CNN en- coder and an NNLM decoder, Feat2s (Nallap- ati et ...an attentive CNN encoder and an Elman RNN decoder, and SEASS (Zhou et ... See full document
11
Abstractive Compression of Captions with Attentive Recurrent Neural Networks
... longer sentence, but rather describe an ...an abstractive summarization ...true abstractive compressions, and we see many applications in typical NLG tasks and real world ap- ...from ... See full document
10
Controllable Abstractive Summarization
... adversarial networks (Goodfellow et ...manipulating sentence sentiment and Sennrich et ...polite neural machine translation ...for sentence compression using decoding-time restrictions and ... See full document
10
Towards Abstractive Multi Document Summarization Using Submodular Function Based Framework, Sentence Compression and Merging
... document summarization are extractive which principally based on two im- portant objectives, namely maximizing the rele- vance and minimizing the redundancy (Carbonell and Goldstein, 1998; Erkan and Radev, ...of ... See full document
7
Improving Neural Abstractive Document Summarization with Structural Regularization
... document summarization, but both the seq2seq models and the basic hierarchical encoder-decoder models are not yet able to capture them ...long sentence to generate a more concise one or compress several ... See full document
10
Unsupervised Abstractive Meeting Summarization with Multi Sentence Compression and Budgeted Submodular Maximization
... and summarization) into a uni- fied, fully unsupervised end-to-end summarization framework, and introduces some novel compo- ...grammatical abstractive summaries despite taking noisy utter- ances as ... See full document
11
Survey Paper on Text Summarization Methods
... Extractive Summarization does its work by choosing a subset of already existing and important words, phrases or sentences from the original document in order to form ...first sentence of the summary ...text ... See full document
6
fairseq: A Fast, Extensible Toolkit for Sequence Modeling
... Inference. FAIRSEQ provides fast inference for non-recurrent models (Gehring et al., 2017; Vaswani et al., 2017; Fan et al., 2018b; Wu et al., 2019) through incremental decoding, where the model states of ... See full document
6
Cutting off Redundant Repeating Generations for Neural Abstractive Summarization
... Shiqi Shen, Yong Cheng, Zhongjun He, Wei He, Hua Wu, Maosong Sun, and Yang Liu. 2016. Mini- mum risk training for neural machine translation. In Proceedings of the 54th Annual Meeting of the As- sociation for ... See full document
7
Text Summarization using Neural Network Theory
... Each sentence is represented as a vector [f1, ...artificial neural networks to produce summaries of news ...A neural network is trained on a corpus of articles. The neural network is ... See full document
7
Abstractive Document Summarization with a Graph Based Attentional Neural Model
... state-of-the-art abstractive model (Distraction- M3) on the CNN dataset, as shown in Table ...state-of-the-art neural summariza- tion methods reported in recent ...a neural abstrac- tive baseline ... See full document
11
Related subjects