Hierarchical Text Generation using an Outline
@article{Drissi2018HierarchicalTG, title={Hierarchical Text Generation using an Outline}, author={Mehdi Drissi and Olivia Watkins and Jugal Kumar Kalita}, journal={ArXiv}, year={2018}, volume={abs/1810.08802} }
Many challenges in natural language processing require generating text, including language translation, dialogue generation, and speech recognition. For all of these problems, text generation becomes more difficult as the text becomes longer. Current language models often struggle to keep track of coherence for long pieces of text. Here, we attempt to have the model construct and use an outline of the text it generates to keep it focused. We find that the usage of an outline improves perplexity…
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References
SHOWING 1-10 OF 23 REFERENCES
Generating Wikipedia by Summarizing Long Sequences
- Computer ScienceICLR
- 2018
It is shown that generating English Wikipedia articles can be approached as a multi- document summarization of source documents and a neural abstractive model is introduced, which can generate fluent, coherent multi-sentence paragraphs and even whole Wikipedia articles.
Hierarchical Neural Story Generation
- Computer ScienceACL
- 2018
This work collects a large dataset of 300K human-written stories paired with writing prompts from an online forum that enables hierarchical story generation, where the model first generates a premise, and then transforms it into a passage of text.
Globally Coherent Text Generation with Neural Checklist Models
- Computer ScienceEMNLP
- 2016
The neural checklist model is presented, a recurrent neural network that models global coherence by storing and updating an agenda of text strings which should be mentioned somewhere in the output, and demonstrates high coherence with greatly improved semantic coverage of the agenda.
Text Summarization
- Computer ScienceEncyclopedia of Database Systems
- 2009
This thesis deals with the development of a new text summarization method that uses the latent semantic analysis (lsa). The language-independent analysis is able to capture interrelationships among…
Cold Fusion: Training Seq2Seq Models Together with Language Models
- Computer ScienceINTERSPEECH
- 2018
It is shown that Seq2Seq models with Cold Fusion are able to better utilize language information enjoying i) faster convergence and better generalization, and ii) almost complete transfer to a new domain while using less than 10% of the labeled training data.
Coarse-to-Fine Attention Models for Document Summarization
- Computer ScienceNFiS@EMNLP
- 2017
A novel coarse-to-fine attention model that hierarchically reads a document, using coarse attention to select top-level chunks of text and fine attention to read the words of the chosen chunks, which achieves the desired behavior of sparsely attending to subsets of the document for generation.
Linguistic Features of Helpfulness in Automated Support for Creative Writing
- Computer ScienceProceedings of the First Workshop on Storytelling
- 2018
An emerging NLP application that supports creative writing by automatically suggesting continuing sentences in a story is examined, and the task of predicting helpfulness based on automatically detected linguistic features of the suggestions is explored.
Neural Machine Translation by Jointly Learning to Align and Translate
- Computer ScienceICLR
- 2015
It is conjecture that the use of a fixed-length vector is a bottleneck in improving the performance of this basic encoder-decoder architecture, and it is proposed to extend this by allowing a model to automatically (soft-)search for parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly.
Sequence to Sequence Learning with Neural Networks
- Computer ScienceNIPS
- 2014
This paper presents a general end-to-end approach to sequence learning that makes minimal assumptions on the sequence structure, and finds that reversing the order of the words in all source sentences improved the LSTM's performance markedly, because doing so introduced many short term dependencies between the source and the target sentence which made the optimization problem easier.
Text Summarization Techniques: A Brief Survey
- Computer ScienceInternational Journal of Advanced Computer Science and Applications
- 2017
The main approaches to automatic text summarization are described and the effectiveness and shortcomings of the different methods are described.