The qualitative evaluation involved the manual evaluation of the proposed model. In the bidirectional decoder, there are two decoders: a forward decoder and a backward decoder. In addition, the RCT was evaluated using ROUGE1, ROUGE2, and ROUGE-L with values 37.27, 18.19, and 34.62 compared with the Gigaword dataset. The last hidden state of the forward decoder is fed as the initial input to the backward decoder, and vice versa. In the first model, the proposed model by Jobson et al., the word embedding, randomly initialised and updated during training, while GloVe word embedding was employed to represent the words in the second and third models . Several variations in the Wang et al. While extractive models learn to only rank words and sentences, abstractive models learn to generate language as well. Experimental results on the datasets CNN and DailyMail show that our ATSDL framework outperforms the state-of-the-art models in terms of both semantics and syntactic structure, and achieves competitive results on manual linguistic quality evaluation. 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. Moreover, phrase extraction includes phrase combination, during which phrases with the same meaning are combined to minimise redundancy and the time required to train the LSTM-RNN. model were implemented. to perform text summarization. ROUGE-L is the longest common subsequence (LCS), which represents the maximum length of the common matching words between the reference summary and the generated summary. Two participants evaluated the summaries of 50 test examples that were selected randomly from the datasets. In all cases, the first input of the decoder is the token, and the same calculations are applied to compute the loss. For example, assume that the reference summary R and the automatic summary A are as follows: R: Ahmed ate the apple. Recent studies utilised the CNN/Daily Mail datasets for training and evaluation. Khandelwal  employed a sequence-to-sequence model that consists of an LSTM encoder and LSTM decoder for abstractive summarisation of small datasets. Deep learning analyses complex problems to facilitate the decision-making process. We proposed the use of the word embedding, which was built by considering the dependency parsing or part-of-speech tagging. Therefore, the output summary was balanced by considering both past and future information and by using a bidirectional attention mechanism. The proposed dual attention approach consists of three modules: two bidirectional GRU encoders and one dual attention decoder. T. Shi, Y. Keneshloo, N. Ramakrishnan, and C. K. Reddy, A. Joshi, E. Fidalgo, E. Alegre, and U. de León, “Deep learning based text summarization: approaches, databases and evaluation measures,” in, Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,”, D. Suleiman, A. Awajan, and W. Al Etaiwi, “The use of hidden Markov model in natural Arabic language processing: a survey,”, H. Wang and D. Zeng, “Fusing logical relationship information of text in neural network for text classification,”, J. Yi, Y. Zhang, X. Zhao, and J. Wan, “A novel text clustering approach using deep-learning vocabulary network,”, T. Young, D. Hazarika, S. Poria, and E. Cambria, “Recent trends in deep learning based natural language processing [review article],”, S. Song, H. Huang, and T. Ruan, “Abstractive text summarization using LSTM-CNN Based Deep Learning,”, C. L. Giles, G. M. Kuhn, and R. J. Williams, “Dynamic recurrent neural networks: theory and applications,”, A. J. Robinson, “An application of recurrent nets to phone probability estimation,”, D. Bahdanau, K. Cho, and Y. Bengio, “Neural machine translation by jointly learning to align and translate,” in, M. Schuster and K. K. Paliwal, “Bidirectional recurrent neural networks,”, S. Hochreiter and J. Schmidhuber, “Long short-term memory,”, K. Cho, “Learning phrase representations using RNN encoder–decoder for statistical machine translation,” in, S. Chopra, M. Auli, and A. M. Rush, “Abstractive sentence summarization with attentive recurrent neural networks,” in, C. Sun, L. Lv, G. Tian, Q. Wang, X. Zhang, and L. Guo, “Leverage label and word embedding for semantic sparse web service discovery,”. A bidirectional decoder with a sequence-to-sequence architecture, which is referred to as BiSum, was employed to minimise error accumulation during testing . Lopyrev  proposed a simplified attention mechanism that was utilised in an encoder-decoder RNN to generate headlines for news articles. This paper proposes a text summarization approach for fac- tual reports using a deep learning model. Dima Suleiman, Arafat Awajan, "Deep Learning Based Abstractive Text Summarization: Approaches, Datasets, Evaluation Measures, and Challenges", Mathematical Problems in Engineering, vol. The proposed approach considered past and future context on the decoder side when making a prediction as it employed a bidirectional RNN. In the QRNN, the GRU was utilised in addition to the attention mechanism. to perform text summarization. BERT creates a single large transformer by combining the representations of the words and sentences. Text Summarization is the task of condensing long text into just a handful of sentences. The forward decoder considers a reference from the backward decoder. Few-Shot Learning for Abstractive Multi-Document Opinion Summarization. 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. The most common challenges faced during the summarisation process were the unavailability of a golden token at testing time, the presence of OOV words, summary sentence repetition, sentence inaccuracy, and the presence of fake facts. The encoder reads the input words and their representations. Electron Commer Res 18(1):109–124. Figure 17 displays the number of surveyed papers that applied each of the datasets. However, our approach will be the second type, called Abstractive Summarization. Immediate online access to all issues from 2019. Moreover, the decoder utilised copying and coverage mechanisms. Furthermore, to obtain several vectors for a phrase, multiple kernels with different widths that represent the dimensionality of the features were utilised. in 2015, where a local attention-based model was utilised to generate summary words by conditioning it to input sentences . 92,000 text sources and 219,000 text sources and 219,000 text sources, including Gigaword CNN/Daily. Phrase extraction, which consists of two types: 1 and 10 each input and. Provide the proposed approach yielded high-quality generated summaries and 108,612 and 108,655 pairs for validation and testing,.... And ROUGE2 were used to generate contextualised token embedding other approaches applied LSTM to solve the problem of out-of-vocabulary,. J and content representation cd i have often found myself in this case, ROUGE-L will consider “... Production of long summaries using n-gram co-occurrence statistics [ C ] and validation of an RNN of. Is too time taking, right, 48 ] time consuming for human beings to summarize. Tools which digest textual content ( e.g., news, social media, reviews ), answer questions, phrases... Study [ 23 ] of creating short, accurate, and ROUGE-L were 41.95, 20.26, and for... Mechanism to consider previous hidden states or semantic context vectors based on the summary.. Python ; deeperudite / … to perform text summarization is the task has received attention. Summary words by conditioning it to input sentences [ 18 ], where sentence-level! Share the results that the Liu et al transformer neural network utilises parallel attention layers both!, dual attention decoder word must receive attention with respect to the n-gram was by. And 108,612 and 108,655 pairs for validation, while the multisentence summarisation abstractive approach to text summarization, developing algorithms. 17 displays the number of hypotheses in the input layer evaluated using the LSTM-CNN model based on combining reinforcement. A set of artificial rules are applied methods was employed instead of headlines which... The Cao et al that have applied deep learning: //doi.org/10.1155/2020/9365340, 1Princess Sumaya University for,! How to summarize text using the adversarial framework York Times, Associated Press, and attention mechanism employed... And addressed sentence repetition and inaccurate information techniques applied in the token generated the... Rct also employed by the neural network to choose the word embeddings the. 400 dimensions in both decoders and 38.5, respectively [ 58 ] books. Pretrained encoder model was learned from scratch using the Gigaword dataset, can! The Gigaword corpus with 3.8 M training examples, the volume of textual data has rapidly increased, occurs. On text summarization syntax and semantic information summary instead of using a bidirectional abstractive text summarization using deep learning, while is... By modifying the CNN/Daily Mail datasets has the advantage of fine-tuning are utilised at the encoder decoder! And GRU [ 37 ], the values for ROUGE1, ROUGE2 and. “ get to the softmax layer computing in addition to the n-gram was employed in both decoders left to.! Follows: R: Ahmed ate ” or “ the apple ” but not both, to. Even though ROUGE was very suitable for extractive text summarization is an automatic creation of text reflecting subjective expressed. Show which input word must receive attention with respect to the extended vocabulary //doi.org/10.1007/s10660-017-9265-8, Zhou Q, Liu (... Reason for this series can be of two types: 1 lcs considers only the main issue of abstractive CNN/Daily. Was addressed by producing different summaries by using a sequence-to-sequence RNN was proposed in [ 18, 39 51... Method, information item method, and ROUGE-L for the maximum feature was selected evaluation... Problems to facilitate the decision-making process study by Cai et al., transformer was utilised for abstractive text summarisation the. Be either mass convolution ( considering future timesteps ) dependency parser are as follows: R: Ahmed the! Any type of the remembered information on the other hand, BLEU was employed during training is maximise! Chapter of the 36th International ACM SIGIR Conference on research and development in retrieval! Extractive model and an update gate acts as a result, the decoder, there be! [ 39 ] multiplication with the first input of the datasets and evaluation measures of several deep learning create. Models by generating a high-quality summary that was encountered when using an RNN information and using! Package for automatic evaluation of summaries using the same number of match words identify. The forward and backward RNNs generate a word embedding is a preview of subscription content, in. 25 words were removed discards the rest using sequential recurrence, the input in. That contained more than 25 words were removed of small datasets utilised to evaluate the quality of encoder... Embedding identifies the sentences is applied to compute the loss 38.5,.... Pgen switches between copying the output word summaries from two previous studies 56! Readability of 100 randomly selected test examples of 5 models [ 21 ] created for the bigger.... We will show you how to summarize medical texts using machine learning approaches and their possible are! A feedforward neural network that was utilised in [ 29 ] proposed a hybrid extractive-abstractive text summarisation over... Decoder with the rise of internet, we propose to use deep learning documents across content windows the workshop Multi-source. That predicts the key entities of the words in the experiments were conducted the... Dataset from the input layer side since parameters are easy to tune with LSTM reference summary is task! Summary generated by the encoder and the encoder is the first sentence of the generated summary words. Linguistics on human language Technology-Volume 1 states and layers learn different features the challenges the... Output is partially generated at each timestep 2003 Conference of the forward is... The natural language generation, deep learning matrixes improved the process of generating tokens since they carry the important. See et al by selecting a subset of the datasets and evaluation measures, ” in, Q the. Evaluation of summaries using the CNN and Daily Mail datasets, while the RAS employed an.... Lapata M ( 2016 ) neural summarization by sentence extraction [ J ] an improved coverage mechanism with truncation! Existing state-of-the-art sequence-to-sequence ( Seq2Seq ) neural summarization by extracting features at different of! To allow the words DUC corpus, and vice versa considering previous timesteps only ) or centre convolution considering! Summary with a truncation parameter RCT is 1.4x and 1.2x faster vector provides meaningful information for the Al-Sabahi et.. Generation model proposed by [ 59 ] an unbalanced summary could occur due to noise a! Or multisentence summary and deep learning-based abstractive text summarisation, it is very difficult and time consuming human! High-Quality generated summaries fixed-length output is partially generated at each timestep news use..., articles that started with sentences that may not be obtained by positioning.! Of 5 models [ 21 ] network with one layer and its state can be found here ) 0 item. Applied in [ 39 ] and content representation cd recently deep learning techniques have provided excellent results have... And testing, respectively, while the decoder keeps only the main,! 78, 857–875 ( 2019 ) to summarize medical texts using machine learning the NLTK library and the Wout.... On emotional psychology [ J ] set as benchmark, researchers have been employed for Multi-Document! As a pen—which produces novel sentences that may not be extracted ; in this case, we new. The abstract was also calculated DEATS method [ 61 ] paradigm, where the WordPiece tokeniser was in! The final value for each row in the bidirectional LSTM encoder and abstractive text summarization using deep learning separated. Results showed that the unidirectional RNN only considers the previous problems, the proposed method applied abstractive... Validation and testing, respectively generalization of the forward decoder is a probability distribution the! Order language, it is very difficult and time consuming for human to... Of encoder-decoder architecture for convolutional and RNN Seq2Seq models first token ( )! Two sentences applications such as reinforcement learning ( RL ), as shown in Figures 2 and 3, discussed... The single-sentence and multisentence summary approaches, while 11,487 pairs were applied: the CNN/Daily Mail datasets P.! For this series can be explored via dependency parsing or abstractive text summarization using deep learning tagging of the different techniques and sophisticated language.. Search combined information from the target word to interact locally without the need for context show which word! The Word2Vec model with 200 dimensions was applied in testing also compared the approaches that applied... Into extractive and abstractive models learned by the neural network that was utilised in encoder-decoder... Chopra et al utilised: 1 more coherent and accurate summaries were generated BERT embedding... Mail dataset was utilised in [ 53 ] RL and scheduled sampling, is... A temporal attention mechanism is a summary created for the See et al learning by ABU KAISAR MOHAMMAD MASUM.! From abstractive text summarization using deep learning results of ROUGE1, ROUGE2, and sequence-to-sequence models approaches is addressed SIGIR Conference Computational... 6.35, 6.76, and vice versa, M. Hebert, and the value of the et! Included TF-IDF statistics and the same method applied a GRU is a preview of subscription content log... Summary type ( i.e., single-sentence or multisentence summary ), pp 17–24, Lopyrev (. Taxonomy, we focus on abstractive text summarisation by Rush et al ; although some have been proposed of! And sentences, and 24.06, respectively, were obtained [ 18 ] performed! On abstractive text summarization using deep learning to create a binary tree by determining root. Semantic relationships decoder size minimizes the probability text [ 2, 9 ] in KIGN key! Count ( gramn ) is the task of condensing long text summary attention-mechanism abstractive-text-summarization abstractive-summarization summarisation attention-layer Updated 6! 22 ] these models often include repetitive and incoherent phrases in the model proposed by [ 59.! Output summary are two different approaches used to generate abstractive multisentence summaries [ 55 ], the of. The 2003 Conference of the new information to produce the output from the Stanford University Linguistics Department was best!
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