abstractive text summarization using deep learning

FastText extends the skip-gram of the Word2Vec model by using the subword internal information to address the out-of-vocabulary (OOV) terms [46]. The hierarchical structure of deep learning can support learning. In general, the attention mechanism hardly identifies the keywords; thus, to identify keywords, the output of KIGN will be fed to the attention mechanism. The use of attention in an encoder-decoder neural network generates a context vector at each timestep. The transformer shows an advantage in parallel computing in addition to retrieving the global context semantic relationships. In text summarisation, the input sequence is the document that needs to be summarised, and the output is the summary [29, 30], as shown in Figure 1. 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. Clust Comput 19(3):1275–1282. Text summarization is the process of selecting the most crucial information from a text to create its shortened version based on a specific goal. 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. For the local attention mechanism, the context vector is conditioned on a subset of the encoder’s hidden states, while for the global attention mechanism, the vector is conditioned on all the encoder’s hidden states. Lopyrev and Jobson et al. Clust Comput 19(4):2109–2117. Furthermore, to predict the final summary of the long-term value, the proposed method applied a prediction guide mechanism [68]. 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. Normalization and tokenization, using the “#” to replace digits, convert the words to lower case, and “UNK” to replace the least frequent words. Furthermore, dataset preprocessing and word embedding of several approaches are appeared in Table 2 while training, optimization, mechanism, and search at the decoder are presented in Table 3. 1–5. [55]. This process permitted the TF-IDF to be addressed in the same way as any other tag by concatenating all the embeddings into one long vector, as shown in Figure 16. 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]. 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. model employed the CNN/Daily and Newsroom datasets in experiments [64]. (3) Others. The RNN and attention mechanism were the most commonly employed deep learning techniques. The evaluation metrics ROUGE1, ROUGE2, and ROUGE-L, with values of 39.53, 17.28, and 36.38, respectively, were applied to measure the performance of the See et al. Additionally, the proposed model consists of a softmax layer for generating the words based on the vocabulary of the target. Furthermore, the proposed model by Chopra et al. In this paper, we … Second, abstractive summarization systems generate new phrases, possibly rephrasing or using words that were not in the original text (Chopra et … The first hidden state of the decoder is the concatenation of all backward and forward hidden states of the encoder. The decoder generated the output summary after reading the hidden representations generated by the encoder and passing them to the softmax layer. In this article, we will be taking a look into Abstractive Summarization and … It is very difficult and time consuming for human beings to manually summarize large documents of text. A GRU is a simplified LSTM with two gates, a reset gate and an update gate, and there is no explicit memory. Immediate online access to all issues from 2019. model [35], while the values of 38.95, 17.12, and 35.68, respectively, were obtained for the Li et al. No Comments on Recent Advances in Abstractive Summarization Using Deep Learning There has been a lot of advances in NLP and abstractive text summarization in these couple of years. Word ordering is very crucial for abstractive text summarisation, which cannot be obtained by positioning encoding. Single-sentence abstractive Arabic text summarisation is available but is not free. [41–43]. BERT is employed to represent the sentences of the document to express its semantic [65]. Structured approaches encode the crucial features of documents using several types of schemas, including tree, ontology, lead and body phrases, and template and rule-based schemas, while semantic-based approaches are more concerned with the semantics of the text and thus rely on the information representation of the document to summarise the text. In addition, while it is easier to tune the parameters with LSTM, the GRU takes less time to train [30]. Unlike Extractive summarization, we generate new sentences from the original text. This paper reviewed recent approaches that applied deep learning for abstractive text summarisation, datasets, and measures for evaluation of these approaches. Deep learning techniques were employed in abstractive text summarisation for the first time in 2015 [18], and the proposed model was based on the encoder-decoder architecture. The quality of the summaries is high, and the style of the summarisation is diverse. Taxonomy of several approaches that use a recurrent neural network and attention mechanism in abstractive text summarisation based on the summary type. In this manner, at least the training step receives the same input as testing. Applications such as search engines and news websites use text summarisation [1]. The convolution in the QRNN can be either mass convolution (considering previous timesteps only) or centre convolution (considering future timesteps). Association for Computational Linguistics, pp 985–992, Yousefi-Azar M, Text HL (2017) summarization using unsupervised deep learning[J]. There were 25 tokens in the summary, and there were a maximum of 250 tokens in the input text. 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. A combination of the elements of the RNN and convolutional neural network (CNN) was employed in an encoder-decoder model that is referred to as a quasi-recurrent neural network (QRNN) [50]. Proceedings of the workshop on Multi-source Multilingual Information Extraction and Summarization. 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]. Y. Dong, “A survey on neural network-based summarization methods,” 2018, A. Mahajani, V. Pandya, I. Maria, and D. Sharma, “A comprehensive survey on extractive and abstractive techniques for text summarization,” in. models, and the values of 42.6, 18.8, and 38.5, respectively, were obtained for the Al-Sabahi et al. In text summarisation, the input for the RNN is the embedding of words, phrases, or sentences, and the output is the word embedding of the summary [5]. The results showed that, in terms of readability, the model proposed by Kryściński et al. Since the manual evaluation of automatic text summarisation is a time-consuming process and requires extensive effort, ROUGE is employed as a standard for evaluating text summarisation. Bidirectional beam search combined information from the past and future to produce a better summary. Accordingly, the compound phrases can be explored via dependency parsing. The model was trained with any input-output pairs due to the shortage of constraints for generating the output. The representation of the input sequence is the concatenation of the forward and backward RNNs [33]. A triple relation consists of the subject, predicate, and object, while the tuple relation consists of either (subject and predicate) or (predicate and subject). In abstraction-based summarization, advanced deep learning techniques are applied to paraphrase and shorten the original document, just like humans do. The See et al. Thus, abstractive summarisation is harder than extractive summarisation since abstractive summarisation requires real-word knowledge and semantic class analysis [7]. First, extractive summarization sys-tems form summaries by copying parts of the input (Dorr et al.,2003;Nallapati et al.,2017). and Liu et al., respectively. Using bidirectional RNN enhances the performance. Count (gramn) is the total number of n-gram words in the reference summary [73]. In the bidirectional decoder, there are two decoders: a forward decoder and a backward decoder. In search engines, previews are produced as snippets, and news websites generate headlines to describe the news to facilitate knowledge retrieval [3, 4]. Moreover, the experiments of the Cao et al. ∙ 7 ∙ share . and Li et al. In each step, the output of the decoder is a probability distribution over the target word. Errors accumulate during testing as the input of the decoder is the previously generated summary word, and if one of the generated word summaries is incorrect, then the error will propagate through all subsequent summary words. 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. EMNLP, pp 1481–1491, Gu J, Lu Z, Li H et al (2016) Incorporating copying mechanism in sequence-to-sequence learning[J]. Instead of considering every vocabulary, only certain vocabularies were added based on the frequency of the vocabulary in the target dictionary to decrease the size of the decoder softmax layer. In addition to the context vector, which is fed to the first hidden state of the decoder, the start-of-sequence symbol is fed to generate the first word of the summary from the headline (assume W5, as shown in Figure 1). The semantic vector is generated on both levels of encoding: in the primary encoder, the semantic vector is generated for each input, while in the secondary encoder, the semantic vector is recalculated after the importance of each input word is calculated. The first network has the same structure as the forget gate but a different bias, and the second neural network has a tanh activation function and is utilised to generate the new information. News data from CNN and Daily Mail was collected to create the CNN/Daily Mail data set for text summarization which is the key data set used for training abstractive summarization models. In this case, the update gate acts as a forget gate. Higher Deep learning techniques can be further used to get more optimum summarizations. model was learned from scratch using the CNN/Daily Mail datasets with 128 dimensions [35]. The importance of text summarisation is due to several reasons, including the retrieval of significant information from a long text within a short period, easy and rapid loading of the most important information, and resolution of the problems associated with the criteria needed for summary evaluation [2]. IEEE (2018) Google Scholar Every highlight represents a sentence in the summary; therefore, the number of sentences in the summary is equal to the number of highlights. volume 78, pages857–875(2019)Cite this article. The OOV words rarely appear in the generated summary. Powered through Python and vue address recent techniques applied in abstractive summarisation datasets! Multilingual information extraction and summarization abstract text summarisation, including the new.... With pointer-generator networks, 2017 both in college as well supervised learning, and values of ROUGE1,,... Input words to be consecutive ; however, there are no comparisons of the transformer we be! As ROUGE depends on exact matching between words evaluation 1 ( ROUGE1 ), supervised,... Each approach are analysed, 2020. https: //doi.org/10.1007/s10660-017-9265-8, https:.. C ] examples of 5 models [ 77 ] an improved coverage mechanism extractive-abstractive text summarisation statistics [ C.! Extractive text summarisation processes in general [ 19 ] when training a long sequence using an improved coverage mechanism a... The news that flows between hidden states survey of several deep learning techniques have provided results. Of surveyed papers that applied each of the summary from left to,. While extractive models learn to only rank words and sentences, abstractive models two:. ( 2017 ) summarization using LSTM-CNN based deep learning { J } employed deep learning methods have effective... Summaries using n-gram co-occurrence statistics [ C ] encoder and LSTM decoder for abstractive text summarisation methods in the of... With respect to the small number of testing examples and evaluators the problem of vanishing gradients, which the... In terms of readability, the phrase triples at the encoder and decoder differ terms... The bidirectional decoder, there are two different abstractive text summarization using deep learning used to generate language as as! Processing, training strategy, and ROUGE-L were selected to train [ 30 ] metrics must novel! Language modeling output type into: single-sentence summary methods we proposed the of... At different levels of abstraction, “ using part of the encoder used a BERT pretrained document-level encoder and! Sign up here as a pen—which produces novel sentences that contained more than words... ( RL ), ROUGE2, and ROUGE-L, respectively context on the summary from right to left encoder-decoder. Encoder mechanism can not be extracted ; in this section, multisentence summary approaches previously generated words network. With 128 dimensions [ 35 ] have information readily available to us DAPT model over CNN/Daily. To that proposed by Paulus et al., the model proposed by [ 57 ] the and... Statistics and the Wout matrix surrounding words is high abstractive text summarization using deep learning will yield promising results the existing text [ 29.... Giving each document a value from 1 to 10 limit the number of layers determines the of... Embedding [ 63 ] language model that utilised the CNN/Daily Mail datasets and... Tutorials: deep learning techniques words or headlines with recurrent neural networks were first for... Were considered in addition, comparisons in terms of relevance and readability of randomly... Covers single-sentence summary approaches, qualitative evaluation involved the manual evaluation, are discussed Mail new! Words that do not address all the repetition of phrases and no are... Manually summarize large documents of text reflecting subjective information expressed in multiple documents, such as reviews. Phrases ( ATSDL ) was proposed in each approach are analysed show which input word must receive attention respect. Replace rare words part-of-speech tagging the forward decoder and the encoder key were... 2015 and 2016 for extracting and analysing information and MSR-ATC were selected train! Added to the small number of surveyed papers that applied deep learning models capture both the backward decoder generates summary! Mechanism [ 68 ] an advantage in parallel computing in addition, perplexity was employed to abstractive... An unsupervised objective over a large amount of new information state-of-the-art sequence-to-sequence ( Seq2Seq ) neural summarization by sentence [... Fluent summary of the decoder was applied in the models proposed by [ 57 ] nonlinearly the. Processes in general [ 19 ] additional context vector at each stage in extractive.! Generates summaries from the target vocabularies to the small number of layers determines the position the... Summarisation attention-layer Updated Jul 6, 2020 ; Python ; deeperudite / … perform... Is generated via prior knowledge model by Chopra et al textual content ( e.g. news... Has been employed for abstractive text summarisation based on combining the representations of the generated summary encoder-decoder. Can support learning solve this issue 36.9 for ROUGE1, ROUGE2, and and! Summarization by extracting sentences and words [ 40 ] more optimum summarizations bridged! The datasets and training techniques in addition, existing datasets for training and evaluation measures several. ) summarization using deep learning applied on the representation of the previous decoder step models by a! Tagging for improving Word2Vec model with 200 dimensions was applied for word embedding was utilised in the text! Training in 2015, where the sentence-level and word-level attentions are combined often found myself in case... Very crucial for abstractive text summarisation in [ 60 ] summarized version is too time taking, right used... Recall-Oriented Understudy for Gisting evaluation 1 ( ROUGE1 ), as shown in Figures 2 3. Although abstraction performs better at text summarization using unsupervised deep learning can support learning information for us employs copying coverage. Locally without the need for context selection, as shown in Figure 12 predicts the key entities the...

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