Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond

Nallapati, Ramesh; Zhou, Bowen; Santos, Cícero Nogueira dos; Gülçehre, Çağlar; Xiang, Bing

doi:10.18653/v1/k16-1028

Cited by 1,760 publications

(1,621 citation statements)

References 20 publications

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“…7 Preliminary experiments training the models proposed by Rush et al (2015) and Nallapati et al (2016) on our dataset have been promising: by manual inspection of individual samples, they produce useful summaries for many Reddit posts; we leave a quantitative evaluation for future work.…”

Section: Resultsmentioning

confidence: 99%

TL;DR: Mining Reddit to Learn Automatic Summarization

Völske

Potthast

Syed

et al. 2017

Proceedings of the Workshop on New Frontiers in Summarization

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Recent advances in automatic text summarization have used deep neural networks to generate high-quality abstractive summaries, but the performance of these models strongly depends on large amounts of suitable training data. We propose a new method for mining social media for author-provided summaries, taking advantage of the common practice of appending a "TL;DR" to long posts. A case study using a large Reddit crawl yields the Webis-TLDR-17 corpus, complementing existing corpora primarily from the news genre. Our technique is likely applicable to other social media sites and general web crawls.

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Section: Resultsmentioning

confidence: 99%

TL;DR: Mining Reddit to Learn Automatic Summarization

Völske

Potthast

Syed

et al. 2017

Proceedings of the Workshop on New Frontiers in Summarization

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“…In Table 4 we compare our model with the abstractive attentional encoder-decoder models in ( Nallapati et al, 2016), which leverage several effective techniques and achieve state-of-the-art performance on sentence abstractive summarization tasks. The words-lvt2k and words-lvt2k-ptr are flat models and words-lvt2k-hieratt is a hierarchical extension.…”

Section: Discussionmentioning

confidence: 99%

“…Cheng and Lapata (2016) also adopt a word extraction model, which is restricted to use the words of the source document to generate a summary, although the performance is much worse than the sentence extractive model. Nallapati et al (2016) extend the sentence summarization model by trying a hierarchical attention architecture and a limited vocabulary during the decoding phase. However these models still investigate few properties of the document summarization task.…”

Section: Abstractive Summarization Methodsmentioning

confidence: 99%

Abstractive Document Summarization with a Graph-Based Attentional Neural Model

Tan¹,

Wan²,

Xiao³

2017

Proceedings of the 55th Annual Meeting of the Association For Computational Linguistics (Volume 1: Long Papers)

295

257

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Abstractive summarization is the ultimate goal of document summarization research, but previously it is less investigated due to the immaturity of text generation techniques. Recently impressive progress has been made to abstractive sentence summarization using neural models. Unfortunately, attempts on abstractive document summarization are still in a primitive stage, and the evaluation results are worse than extractive methods on benchmark datasets. In this paper, we review the difficulties of neural abstractive document summarization, and propose a novel graph-based attention mechanism in the sequence-to-sequence framework. The intuition is to address the saliency factor of summarization, which has been overlooked by prior works. Experimental results demonstrate our model is able to achieve considerable improvement over previous neural abstractive models. The data-driven neural abstractive method is also competitive with state-of-the-art extractive methods.

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“…In the fields of NLP, the popular Seq2Seq [16] model is based on RNN/LSTM, in which a multi-layer of an LSTM network is used as an encoder and another multi-layer of the LSTM network is used as a decoder. This kind of Seq2Seq model has many variants and has been applied in many applications, such as machine translation [17], text summary generation [18], and Chinese poetry generation [19], among others.…”

Section: Recurrent Neural Network In Nlpmentioning

confidence: 99%

Long Length Document Classification by Local Convolutional Feature Aggregation

Liu

Cong

et al. 2018

Algorithms

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Abstract:The exponential increase in online reviews and recommendations makes document classification and sentiment analysis a hot topic in academic and industrial research. Traditional deep learning based document classification methods require the use of full textual information to extract features. In this paper, in order to tackle long document, we proposed three methods that use local convolutional feature aggregation to implement document classification. The first proposed method randomly draws blocks of continuous words in the full document. Each block is then fed into the convolution neural network to extract features and then are concatenated together to output the classification probability through a classifier. The second model improves the first by capturing the contextual order information of the sampled blocks with a recurrent neural network. The third model is inspired by the recurrent attention model (RAM), in which a reinforcement learning module is introduced to act as a controller for selecting the next block position based on the recurrent state. Experiments on our collected four-class arXiv paper dataset show that the three proposed models all perform well, and the RAM model achieves the best test accuracy with the least information.

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Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond

Cited by 1,760 publications

References 20 publications

TL;DR: Mining Reddit to Learn Automatic Summarization

TL;DR: Mining Reddit to Learn Automatic Summarization

Abstractive Document Summarization with a Graph-Based Attentional Neural Model

Long Length Document Classification by Local Convolutional Feature Aggregation

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