Efficient GAN-based Method for Extractive Summarization (original) (raw)

Abstractive Text Summarization Using GAN

ijisrt, 2024

In the field of natural language processing, the task of writing long concepts into short expressions has attracted attention due to its ability to simplify the processing and understanding of information. While traditional transcription techniques are effective to some extent, they often fail to capture the essence and nuances of the original texts. This article explores a new approach to collecting abstract data using artificial neural networks (GANs), a class of deep learning models known for their ability to create patterns of real information. We describe the fundamentals of text collection through a comprehensive review of existing literature and methods and highlight the complexity of GAN-based text. Our goal is to transform complex text into context and meaning by combining the power of GANs with natural language understanding. We detail the design and training of an adaptive GAN model for the text recognition task. We also conduct various experiments and evaluations using established metrics such as ROUGE and BLEU scores to evaluate the effectiveness and efficiency of our approach. The results show that GANs can be used to improve the quality and consistency of generated content, data storage, data analysis paper, etc. It shows its promise in paving the way for advanced applications in fields. Through this research, we aim to contribute to the continued evolution of writing technology, providing insights and innovations that support the field to a new level of well-done.

SHEG: summarization and headline generation of news articles using deep learning

Neural Computing and Applications, 2020

The human attention span is continuously decreasing, and the amount of time a person wants to spend on reading is declining at an alarming rate. Therefore, it is imperative to provide a quick glance of important news by generating a concise summary of the prominent news article, along with the most intuitive headline in line with the summary. When humans produce summaries of documents, they not only extract phrases and concatenate them but also produce new grammatical phrases or sentences that coincide with each other and capture the most significant information of the original article. Humans have an incredible ability to create abstractions; however, automatic summarization is a challenging problem. This paper aims to develop an end-to-end methodology that can generate brief summaries and crisp headlines that can capture the attention of readers and convey a significant amount of relevant information. In this paper, we propose a novel methodology known as SHEG, which is designed as a hybrid model. It works by integrating both extractive and abstractive mechanisms using a pipelined approach to produce a concise summary, which is then used for headline generation. Experiments were performed on publicly available datasets, viz. CNN/Daily Mail, Gigaword, and NEWS-ROOM. The results obtained validate our approach and demonstrate that the proposed SHEG model is effectively producing a concise summary as well as a captivating and fitting headline.

Candidate sentence selection for extractive text summarization

Information Processing & Management, 2020

Text summarization is a process of generating a brief version of documents by preserving the fundamental information of documents as much as possible. Although most of the text summarization research has been focused on supervised learning solutions, there are a few datasets indeed generated for summarization tasks, and most of the existing summarization datasets do not have human-generated goal summaries which are vital for both summary generation and evaluation. Therefore, a new dataset was presented for abstractive and extractive summarization tasks in this study. This dataset contains academic publications, the abstracts written by the authors, and extracts in two sizes, which were generated by human readers in this research. Then, the resulting extracts were evaluated to ensure the validity of the human extract production process. Moreover, the extractive summarization problem was reinvestigated on the proposed summarization dataset. Here the main point taken into account was to analyze the feature vector to generate more informative summaries. To that end, a comprehensive syntactic feature space was generated for the proposed dataset, and the impact of these features on the informativeness of the resulting summary was investigated. Besides, the summarization capability of semantic features was experienced by using GloVe and word2vec embeddings. Finally, the use of ensembled feature space, which corresponds to the joint use of syntactic and semantic features, was proposed on a long short-term memory-based neural network model. ROUGE metrics evaluated the model summaries, and the results of these evaluations showed that the use of the proposed ensemble feature space remarkably improved the single-use of syntactic or semantic features. Additionally, the resulting summaries of the proposed approach on ensembled features prominently outperformed or provided comparable performance than summaries obtained by state-ofthe-art models for extractive summarization.

NUTS: Network for Unsupervised Telegraphic Summarization

2018

Extractive summarization methods operate by ranking and selecting the sentences which best encapsulate the theme of a given document. They do not fare well in domains like fictional narratives where there is no central theme and core information is not encapsulated by a small set of sentences. For the purpose of reducing the size of the document while conveying the idea expressed by each sentence, we need more sentence specific methods. Telegraphic summarization, which selects short segments across several sentences, is better suited for such domains. Telegraphic summarization captures the plot better by retaining shorter versions of each sentence while not really concerning itself with grammatically linking these segments. In this paper, we propose an unsupervised deep learning network (NUTS) to generate telegraphic summaries. We use multiple encoderdecoder networks and learn to drop portions of the text that are inferable from the chosen segments. The model is agnostic to both sen...

Generating Topic-Oriented Summaries Using Neural Attention

Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Summarizing a document requires identifying the important parts of the document with an objective of providing a quick overview to a reader. However, a long article can span several topics and a single summary cannot do justice to all the topics. Further, the interests of readers can vary and the notion of importance can change across them. Existing summarization algorithms generate a single summary and are not capable of generating multiple summaries tuned to the interests of the readers. In this paper, we propose an attention based RNN framework to generate multiple summaries of a single document tuned to different topics of interest. Our method outperforms existing baselines and our results suggest that the attention of generative networks can be successfully biased to look at sentences relevant to a topic and effectively used to generate topic-tuned summaries.

Summary Generation Using Deep Learning

IEEE EXPLORE, 2021

Summarization is the process of converting any given long text into a lossy compressed version while preserving the overall essence and meaning of the original given text. A problem in natural language processing is the task of summarizing a voluminous text. This research, lays emphasis on abstractive summarization of the text, a process which generates a coherent, short and meaningful text by learning the context from given input source text. This work is a preliminary attempt to generate abstractive summary. The proposed work uses the Attentional Encoder and Decoder based Sequence to Sequence Recurrent Neural Network model. The proposed research's primary objective is to get insights on the working of the mentioned Sequence architecture and the intricacies of different subcomponents of the encoders and the decoders architecture working together so as to give a particular end result. The scope of this paper covers an experimentation of performing text summarization process using Encoder and Decoder based Sequential Learning Recurrent Neural Network design. Satisfactory summary results are obtained after the model is experimented and analyzed on the Amazon Fine Food reviews.

SummEval: Re-evaluating Summarization Evaluation

Transactions of the Association for Computational Linguistics

The scarcity of comprehensive up-to-date studies on evaluation metrics for text summarization and the lack of consensus regarding evaluation protocols continue to inhibit progress. We address the existing shortcomings of summarization evaluation methods along five dimensions: 1) we re-evaluate 14 automatic evaluation metrics in a comprehensive and consistent fashion using neural summarization model outputs along with expert and crowd-sourced human annotations; 2) we consistently benchmark 23 recent summarization models using the aforementioned automatic evaluation metrics; 3) we assemble the largest collection of summaries generated by models trained on the CNN/DailyMail news dataset and share it in a unified format; 4) we implement and share a toolkit that provides an extensible and unified API for evaluating summarization models across a broad range of automatic metrics; and 5) we assemble and share the largest and most diverse, in terms of model types, collection of human judgmen...

Abstractive Summarization Improved by WordNet-based Extractive Sentences

arXiv (Cornell University), 2018

Recently, the seq2seq abstractive summarization models have achieved good results on the CNN/Daily Mail dataset. Still, how to improve abstractive methods with extractive methods is a good research direction, since extractive methods have their potentials of exploiting various efficient features for extracting important sentences in one text. In this paper, in order to improve the semantic relevance of abstractive summaries, we adopt the WordNet based sentence ranking algorithm to extract the sentences which are most semantically to one text. Then, we design a dual attentional seq2seq framework to generate summaries with consideration of the extracted information. At the same time, we combine pointer-generator and coverage mechanisms to solve the problems of out-of-vocabulary (OOV) words and duplicate words which exist in the abstractive models. Experiments on the CNN/Daily Mail dataset show that our models achieve competitive performance with the state-of-theart ROUGE scores. Human evaluations also show that the summaries generated by our models have high semantic relevance to the original text.

VAE-PGN based Abstractive Model in Multi-stage Architecture for Text Summarization

Proceedings of the 12th International Conference on Natural Language Generation, 2019

This paper describes our submission to the TL;DR challenge. Neural abstractive summarization models have been successful in generating fluent and consistent summaries with advancements like the copy (Pointer-generator) and coverage mechanisms. However, these models suffer from their extractive nature as they learn to copy words from the source text. In this paper, we propose a novel abstractive model based on Variational Autoencoder (VAE) to address this issue. We also propose a Unified Summarization Framework for the generation of summaries. Our model eliminates non-critical information at a sentencelevel with an extractive summarization module and generates the summary word by word using an abstractive summarization module. To implement our framework, we combine submodules with state-of-the-art techniques including Pointer-Generator Network (PGN) and BERT while also using our new VAE-PGN abstractive model. We evaluate our model on the benchmark Reddit corpus as part of the TL;DR challenge and show that our model outperforms the baseline in ROUGE score while generating diverse summaries.