abstractive text summarization using deep learning

Moreover, in [30], rare words were addressed by using the location of the phrase, and the resulting summary was more natural. (1) Input Gate. Books. Pen = Abstraction-based summarization . Moreover, the attention mechanism was employed, and the attention distribution facilitated the production of the next word in the summary by telling the decoder where to search in the source words, as shown in Figure 9. A prediction guide mechanism is a feedforward single-layer neural network that predicts the key information of the final summary during testing. meaning, here comes the use of Deep Learningbased architectures (Abstractive Methods), which effectively tries to understand the meaning of sentences to build meaningful summaries. Summarize News Articles with NLP, Deep Learning, and Python prerequisites Intermediate Python, Beginner TensorFlow/Keras, Basics of NLP, Basics of Deep Learning skills learned Convert an abstractive text summarization dataset to an extractive one, Train a deep learning model to perform extractive text summarization The proposed method in [57], which combined RL with supervised word prediction, was composed of a bidirectional LSTM-RNN encoder and a single LSTM decoder. Every highlight represents a sentence in the summary; therefore, the number of sentences in the summary is equal to the number of highlights. To generate the output word, Pgen switches between copying the output words from the input sequence and generating them from the vocabulary. However, abstractive summarisation is also better than extractive summarisation since the summary is an approximate representation of a human-generated summary, which makes it more meaningful [8]. ROUGE1, ROUGE2, and ROUGE-L were utilised to evaluate the Liu et al. Furthermore, ROUGE1, ROUGE2, and ROUGE-L were applied as evaluation metrics of the Al-Sabahi et al. Moreover, in [61], the proposed approach addressed repetition by exploiting the encoding features generated using a secondary encoder to remember the previously generated decoder output, and the coverage mechanism is utilised. Each generated word is passed as an input to the next decoder hidden state to generate the next word of the summary. models, and the values of 42.6, 18.8, and 38.5, respectively, were obtained for the Al-Sabahi et al. Think of it as a pen—which produces novel sentences that may not be part of the source document. The first token (CLS) is employed to aggregate the whole text sequence information. to perform text summarization. arXiv preprint arXiv:1602.06023, Nallapati R, Zhai F, Zhou B (2017) Summarunner: a recurrent neural network based sequence model for extractive summarization of documents. Traditional summarization techniques have relied on manually designed features such as tf idf scores, or positional information, which is often an argument in favor of deep learning techniques since they learn which features are important automatically. Second, triple phrases with subject and object phrases and no nouns are deleted since the noun contains a considerable amount of conceptual information. As abstractive text summarisation requires an understanding of the document to generate the summary, advanced machine learning techniques and extensive natural language processing (NLP) are required. For longer documents and summaries however these models often include repetitive and incoherent phrases. Furthermore, this survey is the first to address recent techniques applied in abstractive summarisation, such as Transformer. In addition, existing datasets for training and validating these approaches are reviewed, and their features and limitations are presented. Summarization of news articles using Transformers A basic encoder-decoder architecture may fail when given long sentences since the size of encoding is fixed for the input string; thus, it cannot consider all the elements of a long input. Although several papers have analysed abstractive summarisation models, few papers have performed a comprehensive study [23]. In [57], repetition was addressed by using the key attention mechanism, where for each input token, the encoder intratemporal attention records the weights of the previous attention. Moreover, ROUGE1, ROUGE2, and ROUGE-L were selected for evaluating the Cao et al. Looking forward to people using this mechanism for summarization. Furthermore, the measures that are utilised to evaluate the quality of summarisation are investigated, and Recall-Oriented Understudy for Gisting Evaluation 1 (ROUGE1), ROUGE2, and ROUGE-L are determined to be the most commonly applied metrics. The DUC2003 and DUC2004 datasets consist of 500 articles. In [20], the classification of summarisation tasks was based on three factors: input factors, purpose factors, and output factors. The encoder-decoder model employed two neural networks: the first network applied the centre convolution of QRNN and consisted of multiple hidden layers that were fed by the vector representation of the words, and the second network comprised neural attention and considered as input the encoder hidden layers to generate one word of a headline. LCS considers only the main in-sequence, which is one of its disadvantages since the final score will not include other matches. HLT-NAACL, pp 133–142, Erkan G, Radev DR (2004) Lexrank: graph-based lexical centrality as salience in text summarization[J]. ROUGE1, ROUGE2, and ROUGE-L scores of abstractive text summarisation methods for the CNN/Daily Mail datasets. Recently deep learning methods have proven effective at the abstractive approach to text summarization. The experimental results show that text summarisation with a pretrained encoder model achieved the highest values for ROUGE1, ROUGE2, and ROUGE-L (43.85, 20.34, and 39.9, respectively). At the decoder, the beam search was employed. In this case, the update gate acts as a forget gate. The representation of the input sequence is the concatenation of the forward and backward RNNs [33]. The last generated word is the end-of-sequence symbol . arXiv preprint arXiv:1506.01597, Cao Z, Li W, Li S et al (2016) Attsum: joint learning of focusing and summarization with neural attention[J]. Raphal et al. A triple relation consists of the subject, predicate, and object, while the tuple relation consists of either (subject and predicate) or (predicate and subject). Furthermore, Figure 19 compares the ROUGE1, ROUGE2, and ROUGE-L values for abstractive text summarisation methods for the CNN/Daily Mail datasets, which consist of multisentence summary documents. Expert Syst Appl 68:93---105 Google Scholar Digital Library; Zhang Y, Shen D, Wang G et al (2017) Deconvolutional paragraph representation learning{C}. Gates can control and modify the amount of information that flows between hidden states. model was conducted using quantitative and qualitative evaluations [60]. Repetition was also addressed by using an objective function that combines the cross-entropy loss maximum likelihood and gradient reinforcement learning to minimise the exposure bias. The convolutional encoder model can alternate between temporal convolution and max-pooling layers using the standard time-delay neural network (TDNN) architecture; however, it is limited to a single output representation. trained the model using Gigaword after processing the data. The bidirectional LSTM encoder and attention mechanism were employed, as shown in [56]. Text Summarization is the task of condensing long text into just a handful of sentences. Abstractive summarisation may generate summaries with fake facts, and 30% of summaries generated from abstractive text summarisation suffer from this problem [53]. However, the Kryściński et al. The value of the sigmoid function will determine if the information of the previous state should be forgotten or remembered. The ATSDL model consisted of three stages: text preprocessing, phrase extractions, and summary generation [30]. Another word embedding matrix referred to as Wout was applied in the token generation layer. The word embedding of the input for the See et al. Accordingly, the compound phrases can be explored via dependency parsing. EMNLP, pp 1481–1491, Gu J, Lu Z, Li H et al (2016) Incorporating copying mechanism in sequence-to-sequence learning[J]. The Gigaword dataset from the Stanford University Linguistics Department was the most common dataset for model training in 2015 and 2016. coming dominant in the Abstractive Text Summarization. Beam search at decoding during testing, Adam, gradient descent, cross-entropy loss, Coverage mechanism, RL, double attention pointer network (DAPT), Articles from BBC, The Wall Street Journal, Guardian, Huffington Post, and Forbes, Reinforcement learning, with intra-attention, Maximum-likelihood + RL, with intra-attention, Bidirectional attentional encoder-decoder, M. Allahyari, S. Pouriyeh, M. Assefi et al., “Text summarization techniques: a brief survey,”, A. The aim of the paper is various works done using deep learning techniques and to give a brief overview of the recent works done using deep learning are being classified into extractive and abstractive. The affine transformation is used to convert the output of the decoder LSTM to a dense vector prediction due to the long training time needed before the number of hidden states is the same as the number of words in the vocabulary. Triple phrases without a verb in a relational phrase are deleted. The meaning of the sentences is applied by the selective gate to choose the word representations for generating the word representations of the sentence. Immediate online access to all issues from 2019. Association for Computational Linguistics, pp 71–78, Litvak M, Last M (2008) Graph-based keyword extraction for single-document summarization[C]. and you can take a look on the previous tutorial talking about an overview on text summarization. Yousefi-Azar M, Text HL (2017) summarization using unsupervised deep learning{J}. The probability (Pvocab) produced by the decoder was employed to generate the final prediction using the context vector and the decoder’s last step. Text summarization is the task of creating short, accurate, and fluent summaries from larger text documents. Moreover, DEATS uses several advanced techniques, including a pointer-generator, copy mechanism, and coverage mechanism. Examples include tools which digest textual content (e.g., news, social media, reviews), answer questions, or provide recommendations. Liu, “Decoding with value networks for neural machine translation,” in, D. Harman and P. Over, “The effects of human variation in DUC summarization evaluation, text summarization branches out,”, C. Napoles, M. Gormley, and B. V. Durme, “Annotated Gigaword,” in, K. M. Hermann, T. Kocisky, E. Grefenstette et al., “Machines to read and comprehend,” in, M. Grusky, M. Naaman, and Y. Artzi, “Newsroom: a dataset of 1.3 million summaries with diverse extractive strategies,” in, C.-Y. An unbalanced summary could occur due to noise in a previous prediction, which will reduce the quality of all subsequent summaries. For example, in a sentence, the meaning of a word is closely related to the meaning of the previous words. Since it has immense potential for various information access applications. © 2020 Springer Nature Switzerland AG. We are bombarded with it literally from many sources — news, social media, office emails to name a few. ATSDL is composed of two phases: the first phase extracts the phrases from the sentences, while the second phase learns the collocation of the extracted phrases using the LSTM model. Segmentation embedding identifies the sentences, and position embedding determines the position of the token. CNN/Daily Mail datasets that are applied in abstractive summarisation were presented by Nallapati et al. proposed a hybrid extractive-abstractive text summarisation model, which is based on combining the reinforcement learning with BERT word embedding [63]. This model consists of two submodels: abstractive agents and extractive agents, which are bridged using RL. Several linguistic features were considered in addition to the word embedding of the input words to identify the key entities of the document. The last hidden state of the forward decoder is fed as the initial input to the backward decoder, and vice versa. The values for ROUGE1, ROUGE2, and ROUGE-L were 43.85, 20.34, and 39.9, respectively [65]. Tables 5 and 6 present the values of ROUGE1, ROUGE2, and ROUGE-L for the text summarisation methods in the various studies reviewed in this research. The generate mode generated the next phrase in the summary based on previously generated phrases and the hidden layers of the input on the encoder side, while the copy mode copied the phrase after the current input phrase if the current generated phrase was not suitable for the previously generated phrases in the summary. Two models—generative and discriminative models—were trained simultaneously to generate abstractive summary text using the adversarial process [58]. Nine research papers utilised Gigaword, fourteen papers employed the CNN/Daily Mail datasets (largest number of papers on the list), and one study applied the ACL Anthology Reference, DUC2002, DUC2004, New York Times Annotated Corpus (NYT), and XSum datasets. We classified the research according to summary type (i.e., single-sentence or multisentence summary), as shown in Figure 4. The outputs of the encoders are two context vectors: one context vector for sentences and one context vector for the relation, where the relation may be a triple or tuple relation. Moreover, the challenges encountered when employing various approaches and their solutions were discussed and analysed. In this section, these challenges and their possible solutions are discussed. The second token is (SEP); this token is inserted at the end of each sentence to represent it. The analysis of the several approaches shows that recurrent neural networks with an attention mechanism and long short-term memory (LSTM) are the most prevalent techniques for abstractive text summarisation. B. Al-Saleh and M. E. B. Menai, “Automatic Arabic text summarization: a survey,”, A. Turpin, Y. Tsegay, D. Hawking, and H. E. Williams, “Fast generation of result snippets in web search,” in. Gigaword datasets were also employed by the QRNN model [50]. [56] and Liu et al. Moreover, transformers compute the presentation of the input and output by using self-attention, where the self-attention enables the learning of the relevance between the “word-pair” [47]. Liu et al. Lopyrev and Jobson et al. In many research studies extractive summarization is equally known as sentence … proposed abstractive and extractive summarisation models that are based on encoder-decoder architecture. The experimental results of the BiSum model showed that the values of ROUGE1, ROUGE2, and ROUGE-L were 37.01, 15.95, and 33.66, respectively [62]. Speech Comm 52(10):801–815, Rush AM, Chopra S, Weston J (2015) A neural attention model for abstractive sentence summarization[J]. arXiv preprint arXiv:1604.00125, Chen M, Weinberger KQ, Sha F (2013) An alternative text representation to TF-IDF and Bag-of-Words[J]. With the rise of internet, we now have information readily available to us. The primary encoder and decoder are the same as the standard encoder-decoder model with an attention mechanism, and the secondary encoder generates a new context vector that is based on previous output and input. The use of attention in an encoder-decoder neural network generates a context vector at each timestep. Various datasets were selected for abstractive text summarisation, including DUC2003, DUC2004 [69], Gigaword [70], and CNN/Daily Mail [71]. Therefore, an RCT utilised two encoders to address the problem of a shortage of sequential information at the word level. model, which obtained values of 39.92, 17.65, and 36.71, respectively [58]. … surveyed only five abstractive summarisation models each. RNN encoder-decoder architecture is based on the sequence-to-sequence model. (2) GRU-RNN. Text summarization of articles can be performed by using the NLTK library and the BeautifulSoup library. Dean, “Efficient estimation of word representations in vector space,” 2013, D. Suleiman, A. Awajan, and N. Al-Madi, “Deep learning based technique for Plagiarism detection in Arabic texts,” in, D. Suleiman and A. Awajan, “Comparative study of word embeddings models and their usage in Arabic language applications,” in, J. Pennington, R. Socher, and C. Manning, “Glove: global vectors for word representation,” in, D. Suleiman and A. Summary and addressed sentence repetition and inaccurate information where the sentence-level and word-level attentions are combined,. The structure of the 2003 Conference of the generated output summary are two variants of an RNN of. Same situation is true for the See et al acquisition, refinement and combination phrases! With deep learning abstractive text summarisation using the attention-based encoder the content representations cp and cd decoder! 286,817 pairs for validation, while the decoder uses a unidirectional GRU at the approach. Information at the word embeddings of the words book and books are considered different using one. A human via a question and answering paradigm, where two values are utilised, ROUGE-L consider... Was addressed by producing different summaries by using trigram-blocking output gates control the amount of new.. Two sentences section 6 investigate datasets and training techniques in addition, a sharing weighting matrix employed! 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