[PDF] Top 20 Abstractive Document Summarization with a Graph Based Attentional Neural Model
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Abstractive Document Summarization with a Graph Based Attentional Neural Model
... original document. The poor results indicate that even the model is able to learn to identify the salient information in the original document, the performance is limited by the model’s ability of ... See full document
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Improving Neural Abstractive Document Summarization with Structural Regularization
... In document summarization, information com- pression and information coverage are the two most important structural ...properties. Based on the hierarchical structure of document and sum- ... See full document
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An Unsupervised Multi Document Summarization Framework Based on Neural Document Model
... original document set and choosing the top sentences to form the ...Besides, graph based models (Erkan and Radev, 2004; Mihalcea and Tarau, 2005) first measure the sentence similarity then use ... See full document
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Multi News: A Large Scale Multi Document Summarization Dataset and Abstractive Hierarchical Model
... of neural MDS sys- tems. The creation of large-scale multi-document summarization dataset for training has been re- stricted due to the sparsity and cost of human- written ...hierarchical ... See full document
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Graph based Neural Multi Document Summarization
... our model: three using each of the three types of graphs discussed earlier, and one without using any ...each document clus- ter, we tokenize all the documents into sentences and generate a graph ... See full document
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A Semantic Based Approach for Abstractive Multi-Document Text Summarization
... domain. Abstractive summarization will serve as a tool for generating summary which is semantically correct and produced fewer amounts of sentences in ...Extractive summarization leads to sentence ... See full document
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Abstractive Text Image Summarization Using Multi Modal Attentional Hierarchical RNN
... multi-modal summarization research ...Recent neural summarization research shows the strength of the Encoder-Decoder model in text ...an abstractive text-image summarization ... See full document
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Entity Commonsense Representation for Neural Abstractive Summarization
... base. Based on these observations, this pa- per investigates the usage of linked entities to guide the decoder of a neural text summarizer to generate concise and better ...sequence-to-sequence model ... See full document
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Decoupling Encoder and Decoder Networks for Abstractive Document Summarization
... Abstractive document summarization seeks to automatically generate a sum- mary for a document, based on some abstract “understanding” of the original ...unsupervised document ... See full document
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Unsupervised Aspect Based Multi Document Abstractive Summarization
... ion summarization, a multi-document summa- rization task, with an unsupervised abstractive summarization neural ...is based on (i) a language model that is meant to encode ... See full document
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Abstractive Text Summarization using Sequence to sequence RNNs and Beyond
... feed-forward neural network to generate the summary, producing state-of-the-art results on Gigaword and DUC ...volutional model for the encoder, but replaced the decoder with an RNN, producing further ... See full document
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Improving Abstractive Document Summarization with Salient Information Modeling
... seq2seq model on neu- ral translation task, more and more researchers take note of its great potential in text summariza- tion area (Fan et ...seq2seq model with attention mechanism to abstractive ... See full document
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Enhancements to Graph Based Methods for Single Document Summarization
... A Study was conducted in order to clearly assess the relative performance of all the 12 methods. This study results presented throughout the rest of the paper is based on the average of 30 documents. The corpus ... See full document
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Diversity driven attention model for query based abstractive summarization
... 2014), abstractive summarization ((Rush et ...query based abstractive text summarization where the aim is to generate the summary of a document in the context of a ...eral, ... See full document
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Query focused Multi Document Summarization: Combining a Topic Model with Graph based Semi supervised Learning
... LDA based models (W-LDA, S-LDA, Standard- LDA) achieve better ...and document-specific information would influence the per- formance of topic modeling (Chemudugunta et al, 2006), that is why S-LDA and W-LDA ... See full document
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Autoencoder as Assistant Supervisor: Improving Text Representation for Chinese Social Media Text Summarization
... proposed model on a Chi- nese social media ...our model outperforms the state-of-the- art baseline ...our model outperforms the Seq2Seq baseline by the score of ... See full document
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Abstractive Multi document Summarization with Semantic Information Extraction
... some abstractive summarization ap- proaches in recent ...generate abstractive summary by using text-to-text generation to generate sentence for each subject-verb-object triple (Genest and Lapalme, ... See full document
6
Comparative Study of Text Summarization Methods
... sentence based on number of occurrences of the words and then choose the sentence which is having the highest ...methods based on location, title and cue ...a document or first paragraph contains the ... See full document
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Abstractive Sentence Summarization with Attentive Recurrent Neural Networks
... recurrent neural network model for the problem of abstractive sentence ...our model beats the state-of-the-art systems of Rush et ...our model manages to significantly outper- form ... See full document
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DeepChannel: Salience Estimation by Contrastive Learning for Extractive Document Summarization
... RNN based model that decides whether or not to include a sentence in the ...extractive summarization as a sentence ranking task and optimizes the ROUGE evaluation metric through an RL ... See full document
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