In , teacher forcing is employed to address this challenge: during training, instead of feeding the expected word from the headline, 10% of the time, the generated word of the previous step is fed back [75, 76]. As a result, the objective function combined between force learning and maximum likelihood. The CNN/Daily Mail dataset was utilised by Liu et al. A double attention pointer network, which is referred to as (DAPT), was applied to generate an abstractive text summarisation model . T. Mikolov, K. Chen, G. Corrado, and J. Several variations in the Liu et al. to perform text summarization. Therefore, a high-quality dataset needs high effort to become available. The soft switch determines whether to copy the target from the original text or generate it from the vocabulary of the target, as shown in Figure 11. The dual encoder consists of two levels of encoders, i.e., primary and secondary encoders, in addition to one decoder, and all of them employ a GRU. model , which outperformed previous approaches by at least two points in terms of the ROUGE metrics. The use of deep learning Deep learning attempts to imitate what the human brain can achieve by extracting features at different levels of abstraction. Thirty-First AAAI Conference on Artificial Intelligence, pp 3075–3081, Ribeiro R, Marujo L, Martins de Matos D et al (2013) Self reinforcement for important passage retrieval[C]. To generate plausible outputs, abstraction-based summarization approaches must address a wide variety of NLP problems, such as natural language generation, semantic representation, and inference permutation. The objective of training is to maximise the probability of the alignment between the sentence and the summary from both directions. (Tutorial 6) This tutorial is the sixth one from a series of tutorials that would help you build an abstractive text summarizer using tensorflow , today we would build an abstractive text summarizer in tensorflow in an optimized way . Opinion summarization is an automatic creation of text reflecting subjective information expressed in multiple documents, such as user reviews of a product. model . To solve these problems, we would have to shift to abstractive text summarization, but training a neural network for abstractive text summarization requires a lot of computational power and almost 5x more time, and it can not be used on mobile devices efficiently due to limited processing power, which makes it less useful. The output of the sigmoid function is multiplied by the tanh of the new information to produce the output of the current block. Gates can control and modify the amount of information that flows between hidden states. Tax calculation will be finalised during checkout. However, during testing, the input of the forward decoder is the token generated in the previous step. On the other hand, BLEU was employed to evaluate the Lopyrev model , while Khandelwal utilised perplexity . Egonmwan et al. proposed to use sequence-to-sequence and transformer models to generate abstractive summaries . In this hybrid model, a BERT feature-based strategy was used to generate contextualised token embedding. In addition, there are several issues that must be considered in abstractive summarisation, including the dataset, evaluation measures, and quality of the generated summary. The four gates of each LSTM unit, which are shown in Figures 2 and 3, are discussed here. Khandelwal  employed a sequence-to-sequence model that consists of an LSTM encoder and LSTM decoder for abstractive summarisation of small datasets. It is very difficult and time consuming for human beings to manually summarize large documents of text. Their study differentiated between different model architectures, such as reinforcement learning (RL), supervised learning, and attention mechanism. A. Al-Radaideh and D. Q. Bataineh, “A hybrid approach for Arabic text summarization using domain knowledge and genetic algorithms,”, C. Sunitha, A. Jaya, and A. Ganesh, “A study on abstractive summarization techniques in Indian languages,”, D. R. Radev, E. Hovy, and K. McKeown, “Introduction to the special issue on summarization,”, A. Khan and N. Salim, “A review on abstractive summarization methods,”, N. Moratanch and S. Chitrakala, “A survey on abstractive text summarization,” in, S. Shimpikar and S. Govilkar, “A survey of text summarization techniques for Indian regional languages,”, N. R. Kasture, N. Yargal, N. N. Singh, N. Kulkarni, and V. Mathur, “A survey on methods of abstractive text summarization,”, P. Kartheek Rachabathuni, “A survey on abstractive summarization techniques,” in, S. Yeasmin, P. B. Tumpa, A. M. Nitu, E. Ali, and M. I. Afjal, “Study of abstractive text summarization techniques,”, A. Khan, N. Salim, H. Farman et al., “Abstractive text summarization based on improved semantic graph approach,”, Y. Jaafar and K. Bouzoubaa, “Towards a new hybrid approach for abstractive summarization,”, A. M. Rush, S. Chopra, and J. Weston, “A neural attention model for abstractive sentence summarization,” in, N. Raphal, H. Duwarah, and P. Daniel, “Survey on abstractive text summarization,” in. Text summarisation can be divided into extractive and abstractive methods. Extractive technique relies on extraction of key words, whereas in abstractive text summarization technique utilizes the principles of deep learning to generate the required summary. The hierarchical structure of deep learning can support learning. The experimental results of the Chopra et al. Abstractive Text Summarization (ATS), which is the task of constructing summary sentences by merging facts from different source sentences and condensing them into a shorter representation while preserving information content and overall meaning. A bidirectional decoder with a sequence-to-sequence architecture, which is referred to as BiSum, was employed to minimise error accumulation during testing . Furthermore, the pointer-generator technique is applied to point to input words to copy them. Therefore, a new evaluation measure must be proposed to consider the context of the words (words that have the same meaning must be considered the same even if they have a different surface form). In this stage, the secondary encoder generates new hidden states or semantic context vectors hsm, which are fed to the decoder. Gigaword consists of approximately 10 million documents from seven news sources, including the New York Times, Associated Press, and Washington Post. R. Paulus, C. Xiong, and R. Socher, “A deep reinforced model for abstractive summarization,” 2017, K. S. Bose, R. H. Sarma, M. Yang, Q. Qu, J. Zhu, and H. Li, “Delineation of the intimate details of the backbone conformation of pyridine nucleotide coenzymes in aqueous solution,”, C. Li, W. Xu, S. Li, and S. Gao, “Guiding generation for abstractive text summarization based on key information guide network,” in, W. Kryściński, R. Paulus, C. Xiong, and R. Socher, “Improving abstraction in text summarization,” in, K. Yao, L. Zhang, D. Du, T. Luo, L. Tao, and Y. Wu, “Dual encoding for abstractive text summarization,”, X. Wan, C. Li, R. Wang, D. Xiao, and C. Shi, “Abstractive document summarization via bidirectional decoder,” in, Q. Wang, P. Liu, Z. Zhu, H. Yin, Q. Zhang, and L. Zhang, “A text abstraction summary model based on BERT word embedding and reinforcement learning,”, E. Egonmwan and Y. Chali, “Transformer-based model for single documents neural summarization,” in. The one-hot matrix consisted of the number of bin entries, where only one entry was set to one to indicate the value of the TF-IDF of a certain word. For these applications, deep learning techniques have provided excellent results and have been extensively employedinrecentyears. Abstractive summarization involves understanding the text and rewriting it. Three models were employed: the first model applied unidirectional LSTM in both the encoder and the decoder; the second model was implemented using bidirectional LSTM in the encoder and unidirectional LSTM in the decoder; and the third model utilised a bidirectional LSTM encoder and an LSTM decoder with global attention. Kryściński et al. The bidirectional decoder consists of two LSTMs: the forward decoder and the backward decoder. Abstractive Text Summarization (ATS), which is the task of constructing summary sentences by merging facts from different source sentences and condensing them into a shorter representation while preserving information content and overall meaning. Using a bidirectional RNN on the decoder side addressed the problem of summary imbalance. Text summarization can be categorized into two distinct classes: abstractive and extractive. Store highlights is a summary created for the bigger article. Furthermore, ROUGE1, ROUGE2, and ROUGE-L were utilised to evaluate the quality of the summaries. However, there are no comparisons of the quality of several models that generated summaries. summarizing long documents). The use of deep learning architectures in natural language processing entered a new era after the appearance of the sequence to sequence models in the recent decade. We are committed to sharing findings related to COVID-19 as quickly as possible. Therefore, qualitative measures, which can be achieved by manual evaluation, are very important. The decoder was decomposed into a contextual network and pretrained language model, as shown in Figure 12. Single-document text summarization is the task of automatically generating a shorter version of a document while retaining its most important information. Speech Comm 52(10):801–815, Rush AM, Chopra S, Weston J (2015) A neural attention model for abstractive sentence summarization[J]. By shifting the objective towards the learning of inter-window transitions, we circumvent the limitation of existing models which can summarize documents only up … The evaluators graded the output summaries without knowing which model generated them.
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