This work proposes a novel framework for enhancing abstractive text summarization based on the combination of deep learning techniques along with semantic data transformations. Initially, a theoretical model for semantic-based text generalization is introduced and used in conjunction with a deep encoder-decoder architecture in order to produce a summary in generalized form. Subsequently, a methodology is proposed which transforms the aforementioned generalized summary into human-readable form, retaining at the same time important informational aspects of the original text and addressing the problem of out-of-vocabulary or rare words. The overall approach is evaluated on two popular datasets with encouraging results.

Abstractive Text Summarization Based on Deep Learning and Semantic Content Generalization.

A quality abstractive summary should not only copy salient source texts as summaries but should also tend to generate new conceptual words to express concrete details. Inspired by the popular pointer generator sequence-to-sequence model, this paper presents a concept pointer network for improving these aspects of abstractive summarization. The network leverages knowledge-based, context-aware conceptualizations to derive an extended set of candidate concepts. The model then points to the most appropriate choice using both the concept set and original source text. This joint approach generates abstractive summaries with higher-level semantic concepts. The training model is also optimized in a way that adapts to different data, which is based on a novel method of distant-supervised learning guided by reference summaries and testing set. Overall, the proposed approach provides statistically significant improvements over several state-of-the-art models on both the DUC-2004 and Gigaword datasets. A human evaluation of the model’s abstractive abilities also supports the quality of the summaries produced within this framework.

/pdf/concept-pointer-network-for-abstractive-summarization-3u0ergyizr.pdf

Concept Pointer Network for Abstractive Summarization.

With the rapid development of the Internet, the massive amount of web textual data has grown exponentially, which has brought considerable challenges to downstream tasks, such as document management, text classification, and information retrieval. Automatic text summarization (ATS) is becoming an extremely important means to solve this problem. The  core of ATS is to mine the gist of the original text and automatically generate a concise and readable summary. Recently, to better balance and develop these two aspects, deep learning (DL)-based abstractive summarization models have been developed. At present, for ATS tasks, almost all state-of-the-art (SOTA) models are based on DL architecture. However, a comprehensive literature survey is still lacking in the field of DL-based abstractive text summarization. To fill this gap, this paper provides researchers with a comprehensive survey of DL-based abstractive summarization. We first give an overview of abstractive summarization and DL. Then, we summarize several typical frameworks of abstractive summarization. After that, we also give a comparison of several popular datasets that are commonly used for training, validation, and testing. We further analyze the performance of several typical abstractive summarization systems on common datasets. Finally, we highlight some open challenges in the abstractive summarization task and outline some future research trends. We hope that these explorations will provide researchers with new insights into DL-based abstractive summarization.

/pdf/a-comprehensive-survey-of-abstractive-text-summarization-1gl0p5v0.pdf

A Comprehensive Survey of Abstractive Text Summarization Based on Deep Learning

Turkish abstractive text document summarization using text to text transfer transformer

A quality abstractive summary should not only copy salient source texts as summaries but should also tend to generate new conceptual words to express concrete details. Inspired by the popular pointer generator sequence-to-sequence model, this paper presents a concept pointer network for improving these aspects of abstractive summarization. The network leverages knowledge-based, context-aware conceptualizations to derive an extended set of candidate concepts. The model then points to the most appropriate choice using both the concept set and original source text. This joint approach generates abstractive summaries with higher-level semantic concepts. The training model is also optimized in a way that adapts to different data, which is based on a novel method of distantly-supervised learning guided by reference summaries and testing set. Overall, the proposed approach provides statistically significant improvements over several state-of-the-art models on both the DUC-2004 and Gigaword datasets. A human evaluation of the model's abstractive abilities also supports the quality of the summaries produced within this framework.

Concept Pointer Network for Abstractive Summarization

Various efforts have been dedicated to automatically generate coherent, condensed and informative summaries.
Most concentrate on improving the capability of generating neural language models locally, but do not consider global information. In real cases, a summary is comprehensively influenced by the full content of the source text and is especially guided by its core sense.
To seamlessly integrate global semantic representation into a summarization generation system, we propose to incorporate a neural generative topic matrix as an abstractive level of topic information.
By mapping global semantics into a local generative language model, the abstractive summarization is capable of generating succinct and recapitulative words or phrases. Extensive experiments on DUC-2004 and Gigaword datasets convincingly validate the proposed model.

Neural abstractive summarization fusing by global generative topics

The invention discloses a title generation method based on a variational neural network subject model, belonging to the technical field of natural language processing. This method automatically learnsthe document topic hidden distribution vector by variational self-encoder, and combines the document topic hidden distribution vector and the document representation vector learned by multi-layer neural network with attention mechanism, so as to express the comprehensive and deep semantics of the document on the topic and global level, and to construct a high-quality title generation model. Thismethod uses the multi-layer encoder to learn the more comprehensive information of the document, and improves the effect of summarizing the main idea of the full text of the title generation model; the topic implicit distribution vector of VAE learning is utilized, and the document content is represented in the abstract level of topic. The topic implicit distribution vector and the document information learned by the multi-layer encoder are combined with the deep semantic representation and context information to construct a high quality title generation model by using the attention mechanism.

A title generation method based on a variational neural network topic model

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Neural abstractive summarization fusing by global generative topics

A title generation method based on a variational neural network topic model