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13th International Conference on Agents and Artificial Intelligence (ICAART)

作者： Hu, Ting Meinel, Christoph Potsdam Univ Hasso Plattner Inst Potsdam Germany

ISBN: (纸本)9789897584848

Natural Language Generation (NLG) task is to generate natural language utterances from structured data. Seq2seq-based systems show great potentiality and have been widely explored for NLG. While they achieve good generation performance, over-generation and under-generation issues still arise in the generated results. We propose maintaining a masked hard coverage mechanism in the pointer-generator network, a seq2seq-based architecture that trains a switch policy to produce output sequences by partially copying from input structured data. The proposed mechanism can be regarded as the inner controlling module to keep track of the copying history and force the network to generate sentences accurately covering all information provided in structured data. Experimental results show that our coverage mechanism alleviates the over-generation and under-generation issues and achieves decent performance on the E2E NLG dataset.

关键词： Natural Language Generation coverage mechanism Pointer-generator Network

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Extractive-abstractive summarization with pointer and coverage mechanism 18

Extractive-abstractive summarization with pointer and covera...

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2018 International Conference on Big Data Technologies, ICBDT 2018 and its Workshop, 2018 2nd International Conference on Business Information Systems, ICBIS 2018

作者： Zhang, Yong Chen, Erdan Xiao, Weidong System Engineering College National University of Defense Technology Changsha China

ISBN: (纸本)9781450364270

Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization. However, they are facing the challenges of low efficiency and accuracy when dealing with long text: their capability are not enough to handle very long input, they can not reproduce factual details accurately, and they tend to repeat themselves. In this paper, we propose an extractive and abstractive hybrid model. In the extractive part, we construct a graph model and propose a hybrid sentence similarity measure by combining sentence vector and Levenshtein. Then use this measure to rank and extract key sentences and concatenate the key sentences into a shorter text as the input of the summary generator. In the abstractive part, we make two improvement to the standard sequence-to-sequence attentional model. First, we use pointer mechanism to copy words from the source text, which helps the seq2seq generator to handle out-of-vocabulary (OOV) problem. Second, we use coverage mechanism to avoid repetition. We collect a financial news dataset and apply our model to the financial news summarization task, outperforming state-of-the-art method by at least 4.7 ROUGE points. © 2018 Association for Computing Machinery.

关键词： abstractive summarization pointer mechanism coverage mechanism extractive summarization

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Enhancing Abstractive Summarization with Pointer Generator Networks and coverage mechanisms in NLP

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF ELECTRICAL ENGINEERING 2024年第3期48卷 1301-1311页

作者： Yarlagadda, Madhulika Nadendla, Hanumantha Rao MVSR Engn Coll CSE Dept Hyderabad Telangana India CVR Coll Engn Dept CSE AIML Hyderabad Telangana India

The Abstractive summarization strategies have demonstrated potential for crafting summaries that are both credible to readers and relevant to the given situation. This work introduces a more effective technique for abstractive summarization in Natural Language Processing (NLP) through the incorporation of pointer generator networks and coverage mechanisms. Although the goal of abstractive summarization is to present concise summaries suitable for the context, it grapples with challenges such as word consistency and information loss. To address these issues, pointer generator networks are integrated, providing the model with the ability to handle unfamiliar phrases and enhance summary variety. Additionally, coverage mechanisms are employed to ensure that the summary sufficiently encompasses the key ideas from the original text. Experimental results highlight the potential applicability of the proposed model in various NLP applications, demonstrating its superior performance compared to the standard approach.

关键词： Abstractive Summarization Pointer Generator Networks Text Summarization Natural Language Processing coverage mechanism

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Improving Text Summarization Using Feature Extraction Approach Based on Pointer-generator with coverage 1

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18th Web Information Systems and Applications Conference (WISA)

作者： Chen, Yongchao He, Xin Wang, Guanghui Yu, Junyang Henan Prov Engn Res Ctr Intelligent Data Proc Kaifeng Peoples R China Henan Univ Henan Int Joint Lab Intelligent Network Theory & Kaifeng Peoples R China

ISBN: (数字)9783030875718

ISBN: (纸本)9783030875718;9783030875701

The sequence-to-sequence models have been widely used in the abstractive summarization tasks. However, the existing models have inaccuracy issues due to the insufficient understanding of overall semantics, the emergence of Out-Of-Vocabulary (OOV) words and the duplication of generated summarizations. In order to solve the above issues, in this paper, we propose an automatic text summarization model through combining the feature extraction, pointer-generator and coverage mechanism. First, in order to obtain input text with the multiple semantics, a feature extraction method is proposed to extract hidden features in the source text. Then, the pointer-generator (PGN) is used to select words from the source text or vocabulary to form a summarization, which solves the problem of OOV words in the generated summarizations. Finally, the coverage mechanism is applied to reduce the duplication of generated summarizations. The experimental results show that the model achieves better performance on the real-world CNN/Daily Mail summarization dataset.

关键词： Abstractive summarization Sequence-to-sequence Feature extraction coverage mechanism

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Deep learning based sequence to sequence model for abstractive telugu text summarization

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第11期82卷 17075-17096页

作者： Babu, G. L. Anand Badugu, Srinivasu Osmania Univ Univ Coll Engn Hyderabad India Stanley Coll Engn & Technol Women Dept CSE Hyderabad India

With the emergence of deep learning, the attention of researchers has increased significantly towards abstractive text summarization approaches. Though extractive text summarization (ETS) is an important approach, the generated summaries are not always coherent. This paper mainly focuses on the abstractive text summarization (ATS) approach for Telugu language to generate coherent summary. The majority research on ATS approach is conducted in English, while no significant research in Telugu has been documented. An abstractive Telugu text summarization model based on sequence-to-sequence (seq2seq) encoder-decoder architecture is proposed in this paper. The seq2seq model is implemented with bidirectional long short-term memory (Bi-LSTM) based encoder and long short-term memory (LSTM) based decoder. The existing ATS approaches have some drawbacks such as they cannot handle out vocabulary words, attention deficiency issue arising while handling long text sequence and repetition problem. To overcome these issues, some operating mechanisms like pointer generator network, temporal attention mechanism and coverage mechanism are also integrated in the proposed model. Besides, diverse beam search decoding algorithm is also employed to increase the diversity of generated summary. Thus, the proposed seq2seq model is the combination of Bi-LSTM and LSTM based encoder-decoder, pointer generator network, temporal attention mechanism, coverage mechanism and diverse beam search decoding algorithm. The performance of the proposed work is evaluated using the ROUGE toolkit in terms of F-measure, recall and precision. The experimental results of the proposed scheme are evaluated with other existing methods to show that the proposed ATS model outperforms existing Telugu text summarization models.

关键词： Sequence-to-sequence model Temporal attention mechanism coverage mechanism Diverse beam search Pointer generator network

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Multi-document hybrid text summarization with bi-LSTM RNN for Telugu language

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SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 2024年第2期49卷 1-12页

作者： Babu, G. L. Anand Badugu, Srinivasu Anurag Univ Dept Informat Technol Hyderabad 500088 Telangana India Stanley Coll Engn & Technol Women Dept Comp Sci & Engn Hyderabad 500001 Telangana India

One of the most popular south Indian languages in India is the Telugu language which is currently spoken by 84 million native Telugu speakers in Andhra Pradesh and Telangana. With the rapid growth of the Telugu digital content, the need for the automatic text summarizer is arisen to provide short text from huge text documents. Extractive text summarization model generates only significant sentences. Abstractive text summarization method requires more training time. In this paper, a novel hybrid model is proposed for generating text summaries by combining extractive and abstractive approach to reduce the training time. For extractive method TextRank algorithm is utilized and for abstractive method attention-based sequence to sequence model with bidirectional long short-term memory (Bi-LSTM) is utilized. Moreover, coverage mechanism is included into the proposed hybrid approach to reduce the repetition in summaries and to improve the quality of summaries. The performance of the proposed hybrid model is evaluated by the ROUGE toolkit in terms of F-measure, recall and precision. The results of the proposed model are compared with other existing models which shows that the proposed hybrid model outperforms other existing text summarization models for Telugu Language.

关键词： TextRank algorithm sequence to sequence model bidirectional long short-term memory (Bi-LSTM) attention mechanism coverage mechanism ROUGE

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Improving paragraph-level question generation with extended answer network and uncertainty-aware beam search

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INFORMATION SCIENCES 2021年 571卷 50-64页

作者： Zeng, Hongwei Zhi, Zhuo Liu, Jun Wei, Bifan Xi An Jiao Tong Univ Sch Comp Sci & Technol Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ Natl Engn Lab Big Data Analyt Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ Sch Elect Engn Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ Sch Continuing Educ Xian 710049 Shaanxi Peoples R China

Question Generation (QG), which aims to generate a question given the relevant context, is essential to build conversational and question-answering systems. Existing neural question generation models suffer from the inadequate representation of the target answer and inappropriate techniques to reduce repetition. To address these issues, we propose an Extended Answer-aware Network (EAN) which is trained with Word-based coverage mechanism (WCM) and decoded with Uncertainty-aware Beam Search (UBS). The EAN represents the target answer by its surrounding sentence with an encoder and incorporates the extended answer to paragraph representation using gated paragraph-to-answer attention to tackle the problem of the inadequate representation of the target answer. To reduce undesirable repetition, the WCM penalizes repeatedly attending to the same words of different time-steps in the training stage. The UBS incorporates an uncertainty score into beam search to alleviate text degeneration and reduce repeated copying words of the paragraph. Experiments on two benchmark datasets demonstrate the effectiveness of our methods of paragraph-level question generation. Specifically, our model has achieved 4.2% and 27.2% improvement over BLEU-4 compared to the best paragraph-level QG baseline in SQuAD and NewsQA datasets respectively. (c) 2021 Elsevier Inc. All rights reserved.

关键词： Question generation Attention network coverage mechanism Beam search

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Keyphrase Generation Based on Deep Seq2seq Model

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IEEE ACCESS 2018年 6卷 46047-46057页

作者： Zhang, Yong Xiao, Weidong Natl Univ Def Technol Sci & Technol Informat Syst Engn Lab Changsha 410073 Hunan Peoples R China

Keyphrase can provide highly summative information which can help us improve information utilization efficiency in the era of information overload. Though previous researches about keyphrase generation have provided some workable solutions, they generate keyphrase by ranking and selecting meaningful words from the source text. These approaches belong to an extractive method, by which they cannot effectively use semantic meaning of the source text, and are unable to generate keyphrases which do not appear in the source text. So we propose a sequence-to-sequence framework with attention mechanism, copy mechanism, and coverage mechanism, which can effectively deal with the above-mentioned drawbacks. The experimental results on five data sets reveal that our proposed model can achieve a better performance than the traditional extraction approaches and can also generate absent keyphrases which do not appear in the source text.

关键词： Abstraction seq2seq attention mechanism copy mechanism coverage mechanism

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Novel multi‐domain attention for abstractive summarisation

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CAAI Transactions on Intelligence Technology 2023年第3期8卷 796-806页

作者： Chunxia Qu Ling Lu Aijuan Wang Wu Yang Yinong Chen College of Computer Science and Engineering Chongqing University of TechnologyChongqingChina School of Computing and Augmented Intelligence Arizona State UniversityTempeArizonaUSA

The existing abstractive text summarisation models only consider the word sequence correlations between the source document and the reference summary,and the summary generated by models lacks the cover of the subject of source document due to models'small *** order to make up these disadvantages,a multi‐domain attention pointer(MDA‐Pointer)abstractive summarisation model is proposed in this ***,the model uses bidirectional long short‐term memory to encode,respectively,the word and sentence sequence of source document for obtaining the semantic representations at word and sentence ***,the multi‐domain attention mechanism between the semantic representations and the summary word is established,and the proposed model can generate summary words under the proposed attention mechanism based on the words and ***,the words are extracted from the vocabulary or the original word sequences through the pointer network to form the summary,and the coverage mechanism is introduced,respectively,into word and sentence level to reduce the redundancy of sum-mary ***,experiment validation is conducted on CNN/Daily Mail *** evaluation indexes of the model without and with the coverage mechanism are improved respectively,and the results verify the validation of model proposed by this paper.

关键词： abstractive summarisation attention mechanism Bi‐LSTM coverage mechanism pointer network

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Entity Highlight Generation as Statistical and Neural Machine Translation

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2018年第10期26卷 1860-1872页

作者： Huang, Jizhou Sun, Yaming Zhang, Wei Wang, Haifeng Liu, Ting Harbin Inst Technol Res Ctr Social Comp & Informat Retrieval Harbin 150001 Heilongjiang Peoples R China Baidu Inc Beijing 100094 Peoples R China

Entity highlight refers to a short, concise, and characteristic description for an entity, which can be applied to various applications. In this article, we study the problem of automatically generating entity highlights from the descriptive sentences of entities. Specifically, we develop two computational approaches, one is inspired by the statistical machine translation (SMT) and another is a sequence-to-sequence learning (Seq2Seq) approach, which has been successfully applied in neural machine translation and neural summarization. In the Seq2Seq approach, we use attention mechanism, copy mechanism, and coverage mechanism. To generate entity-specific highlights, we also incorporate entity name into the Seq2Seq model to guide the decoding process. We automatically collect large-scale instances as training data without any manual annotation, and ask annotators to create a test set. We compare with several strong baseline methods, and evaluate the approaches with both automatic evaluation and manual evaluation. Experimental results show that the entity enhanced Seq2Seq model with attention, copy, and coverage mechanisms significantly outperforms all other approaches in terms of multiple evaluation metrics.(1)

关键词： Entity highlight generation Seq2Seq model attention mechanism copy mechanism coverage mechanism

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