research-article

Hierarchical Sliding Inference Generator for Question-driven Abstractive Answer Summarization

Authors:

Zijian LiuAuthors Info & Claims

ACM Transactions on Information Systems, Volume 41, Issue 1

Article No.: 7, Pages 1 - 27

https://doi.org/10.1145/3511891

Published: 09 January 2023 Publication History

Abstract

Text summarization on non-factoid question answering (NQA) aims at identifying the core information of redundant answer guidance using questions, which can dramatically improve answer readability and comprehensibility. Most existing approaches focus on extracting query-related sentences to construct a summary, where the logical connection of natural language and the hierarchical interpretable semantic association are often neglected, thus degrading performance. To address these issues, we propose a novel question-driven abstractive answer summarization model, called the Hierarchical Sliding Inference Generator (HSIG), to form inferable and interpretable summaries by explicitly introducing hierarchical information reasoning between questions and corresponding answers. Specifically, we first apply an elaborately designed hierarchical sliding fusion inference model to determine the most relevant question sentence-level representation that provides a deeper interpretable basis for sentence selection in summarization, which further increases computational performance on the premise of following the semantic inheritance structure. Additionally, to improve summary fluency, we construct a double-driven selective generator to integrate various semantic information from two mutual question-and-answer perspectives. Experimental results illustrate that compared with state-of-the-art baselines, our model achieves remarkable improvement on two benchmark datasets and specifically improves the 2.46 ROUGE-1 points on PubMedQA, which demonstrates the superiority of our model on abstractive summarization with hierarchical sequential reasoning.

References

[1]

Mahsa Afsharizadeh, Hossein Ebrahimpour-Komleh, and Ayoub Bagheri. 2018. Query-oriented text summarization using sentence extraction technique. In Proceedings of the 2018 4th International Conference on Web Research. IEEE, 128–132. DOI:

[2]

Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015. Neural machine translation by jointly learning to align and translate. In Proceedings of the 3rd International Conference on Learning Representations.

[3]

Tal Baumel, Matan Eyal, and Michael Elhadad. 2018. Query focused abstractive summarization: Incorporating query relevance, multi-document coverage, and summary length constraints into seq2seq models. CoRR abs/1801.07704. arxiv:1801.07704. Retrieved from http://arxiv.org/abs/1801.07704.

[4]

Samy Bengio, Oriol Vinyals, Navdeep Jaitly, and Noam Shazeer. 2015. Scheduled sampling for sequence prediction with recurrent neural networks. In Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015. 1171–1179. Retrieved from https://proceedings.neurips.cc/paper/2015/hash/e995f98d56967d946471af29d7bf99f1-Abstract.html.

[5]

Santosh Kumar Bharti and Korra Sathya Babu. 2017. Automatic keyword extraction for text summarization: A survey. CoRR abs/1704.03242. arxiv:1704.03242. Retrieved from http://arxiv.org/abs/1704.03242.

[6]

Santosh Kumar Bharti, Korra Sathya Babu, and Anima Pradhan. 2017. Automatic keyword extraction for text summarization in multi-document e-newspapers articles. European Journal of Advances in Engineering and Technology 4, 6 (2017), 410–427.

[7]

Dasha Bogdanova, Jennifer Foster, Daria Dzendzik, and Qun Liu. 2017. If you can’t beat them join them: Handcrafted features complement neural nets for non-factoid answer reranking. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics.Association for Computational Linguistics, 121–131. DOI:

[8]

Sumit Chopra, Michael Auli, and Alexander M. Rush. 2016. Abstractive sentence summarization with attentive recurrent neural networks. In Proceedings of the NAACL HLT 2016, The 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. The Association for Computational Linguistics, 93–98. DOI:

[9]

Daniel Cohen, Liu Yang, and W. Bruce Croft. 2018. WikiPassageQA: A benchmark collection for research on non-factoid answer passage retrieval. In Proceedings of the 41st International ACM SIGIR Conference on Research & Development in Information Retrieval.ACM, 1165–1168. DOI:

Digital Library

[10]

Yang Deng, Wai Lam, Yuexiang Xie, Daoyuan Chen, Yaliang Li, Min Yang, and Ying Shen. 2020. Joint learning of answer selection and answer summary generation in community question answering. In Proceedings of the AAAI Conference on Artificial Intelligence. AAAI,7651–7658. Retrieved from https://aaai.org/ojs/index.php/AAAI/article/view/6266.

[11]

Yang Deng, Wenxuan Zhang, and Wai Lam. 2020. Multi-hop inference for question-driven summarization. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 6734–6744. DOI:

[12]

Yang Deng, Wenxuan Zhang, Yaliang Li, Min Yang, Wai Lam, and Ying Shen. 2020. Bridging hierarchical and sequential context modeling for question-driven extractive answer summarization. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 1693–1696. Retrieved from

Digital Library

[13]

Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 4171–4186. DOI:

[14]

John C. Duchi, Elad Hazan, and Yoram Singer. 2011. Adaptive subgradient methods for online learning and stochastic optimization. The Journal of Machine Learning Research 12, 61 (2011), 2121–2159. Retrieved from http://dl.acm.org/citation.cfm?id=2021068.

Digital Library

[15]

David Kirk Evans, Judith L. Klavans, and Kathleen R. McKeown. 2004. Columbia newsblaster: Multilingual news summarization on the web. In Proceedings of the Demonstration Papers at HLT-NAACL 2004. The Association for Computational Linguistics. Retrieved from https://www.aclweb.org/anthology/N04-3001/.

[16]

Angela Fan, Yacine Jernite, Ethan Perez, David Grangier, Jason Weston, and Michael Auli. 2019. ELI5: Long form question answering. In Proceedings of the 57th Conference of the Association for Computational Linguistics. Association for Computational Linguistics, 3558–3567. DOI:

[17]

Lisa Fan, Dong Yu, and Lu Wang. 2018. Robust neural abstractive summarization systems and evaluation against adversarial information. CoRR abs/1810.06065. arxiv:1810.06065. Retrieved from http://arxiv.org/abs/1810.06065.

[18]

Travis Goodwin, Max E. Savery, and Dina Demner-Fushman. 2020. Towards zero shot conditional summarization with adaptive multi-task fine-tuning. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings. Association for Computational Linguistics, 3215–3226. DOI:

[19]

Helia Hashemi, Mohammad Aliannejadi, Hamed Zamani, and W. Bruce Croft. 2020. ANTIQUE: A non-factoid question answering benchmark. In Proceedings of the Advances in Information Retrieval - 42nd European Conference on IR Research.Springer, 166–173. DOI:

Digital Library

[20]

Johan Hasselqvist, Niklas Helmertz, and Mikael Kågebäck. 2017. Query-based abstractive summarization using neural networks. CoRR abs/1712.06100. arxiv:1712.06100. Retrieved from http://arxiv.org/abs/1712.06100.

[21]

Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural Computation 9, 8 (1997), 1735–1780. DOI:

Digital Library

[22]

Kelvin Jiang, Dekun Wu, and Hui Jiang. 2019. FreebaseQA: A new factoid QA data set matching trivia-style question-answer pairs with freebase. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.Association for Computational Linguistics, 318–323. DOI:

[23]

Qiao Jin, Bhuwan Dhingra, Zhengping Liu, William W. Cohen, and Xinghua Lu. 2019. PubMedQA: A dataset for biomedical research question answering. In Proceedings of the 9th International Joint Conference on Natural Language Processing.Association for Computational Linguistics, 2567–2577. DOI:

[24]

Chandra Khatri, Gyanit Singh, and Nish Parikh. 2018. Abstractive and extractive text summarization using document context vector and recurrent neural networks. CoRR abs/1807.08000. arxiv:1807.08000. Retrieved from http://arxiv.org/abs/1807.08000.

[25]

Virapat Kieuvongngam, Bowen Tan, and Yiming Niu. 2020. Automatic text summarization of COVID-19 medical research articles using BERT and GPT-2. arxiv:2006.01997. Retrieved from https://arxiv.org/abs/2006.01997.

[26]

Philippe Laban, John Canny, and Marti A. Hearst. 2020. What’s the latest? A question-driven news chatbot. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations.Association for Computational Linguistics, 380–387. DOI:

[27]

Logan Lebanoff, Kaiqiang Song, and Fei Liu. 2018. Adapting the neural encoder-decoder framework from single to multi-document summarization. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 4131–4141. DOI:

[28]

Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020. BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.Association for Computational Linguistics, 7871–7880. DOI:

[29]

Zeyu Liang, Junping Du, Yingxia Shao, and Houye Ji. 2021. Gated graph neural attention networks for abstractive summarization. Neurocomputing 431 (2021), 128–136. DOI:

[30]

Chin-Yew Lin. 2004. ROUGE: A package for automatic evaluation of summaries. In Proceedings of the Text Summarization Branches Out. Association for Computational Linguistics, 74–81. Retrieved from https://www.aclweb.org/anthology/W04-1013.

[31]

Junyang Lin, Xu Sun, Shuming Ma, and Qi Su. 2018. Global encoding for abstractive summarization. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 163–169. DOI:

[32]

Jimmy J. Lin, Nitin Madnani, and Bonnie J. Dorr. 2010. Putting the user in the loop: Interactive maximal marginal relevance for query-focused summarization. In Proceedings of the Human Language Technologies: Conference of the North American Chapter of the Association of Computational Linguistics. The Association for Computational Linguistics, 305–308. Retrieved from https://www.aclweb.org/anthology/N10-1041/.

[33]

Fei Liu, Jeffrey Flanigan, Sam Thomson, Norman M. Sadeh, and Noah A. Smith. 2018. Toward abstractive summarization using semantic representations. CoRR abs/1805.10399. arxiv:1805.10399. Retrieved from http://arxiv.org/abs/1805.10399.

[34]

Yang Liu. 2019. Fine-tune BERT for extractive summarization. CoRR abs/1903.10318. arxiv:1903.10318. Retrieved from http://arxiv.org/abs/1903.10318.

[35]

Yang Liu and Mirella Lapata. 2019. Text summarization with pretrained encoders. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing.Association for Computational Linguistics, 3728–3738. DOI:

[36]

Yang Liu and Mirella Lapata. 2019. Text summarization with pretrained encoders. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing.Kentaro Inui, Jing Jiang, Vincent Ng, and Xiaojun Wan (Eds.), Association for Computational Linguistics, 3728–3738. DOI:

[37]

Chirantana Mallick, Ajit Kumar Das, Madhurima Dutta, Asit Kumar Das, and Apurba Sarkar. 2019. Graph-based text summarization using modified TextRank. In Proceedings of the Soft Computing in Data Analytics. Springer, 137–146. DOI:

[38]

Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan T. McDonald. 2020. On faithfulness and factuality in abstractive summarization. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.Association for Computational Linguistics, 1906–1919. DOI:

[39]

Afonso Mendes, Shashi Narayan, Sebastião Miranda, Zita Marinho, André F. T. Martins, and Shay B. Cohen. 2019. Jointly extracting and compressing documents with summary state representations. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.Association for Computational Linguistics, 3955–3966. DOI:

[40]

Rada Mihalcea and Paul Tarau. 2004. TextRank: Bringing order into text. In Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing.ACL, 404–411. Retrieved from https://www.aclweb.org/anthology/W04-3252/.

[41]

Ramesh Nallapati, Bowen Zhou, Cícero Nogueira dos Santos, Çaglar Gülçehre, and Bing Xiang. 2016. Abstractive text summarization using sequence-to-sequence RNNs and Beyond. In Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning.ACL, 280–290. DOI:

[42]

Shashi Narayan, Shay B. Cohen, and Mirella Lapata. 2018. Don’t give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 1797–1807. DOI:

[43]

Shashi Narayan, Shay B. Cohen, and Mirella Lapata. 2018. Ranking sentences for extractive summarization with reinforcement learning. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, 1747–1759. DOI:

[44]

Preksha Nema, Mitesh M. Khapra, Anirban Laha, and Balaraman Ravindran. 2017. Diversity driven attention model for query-based abstractive summarization. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 1063–1072. DOI:

[45]

Dragomir R. Radev, Sasha Blair-Goldensohn, Zhu Zhang, and Revathi Sundara Raghavan. 2001. NewsInEssence: A system for domain-independent, real-time news clustering and multi-document summarization. In Proceedings of the 1st International Conference on Human Language Technology Research. Morgan Kaufmann. Retrieved from https://www.aclweb.org/anthology/H01-1056/.

Digital Library

[46]

Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. Language models are unsupervised multitask learners. In Proceedings of Technical Report, OpenAI.

[47]

Alexander M. Rush, Sumit Chopra, and Jason Weston. 2015. A neural attention model for abstractive sentence summarization. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. The Association for Computational Linguistics, 379–389. DOI:

[48]

Max E. Savery, Asma Ben Abacha, Soumya Gayen, and Dina Demner-Fushman. 2020. Question-driven summarization of answers to consumer health questions. CoRR abs/2005.09067. arxiv:2005.09067. Retrieved from https://arxiv.org/abs/2005.09067.

[49]

Natalie Schluter. 2017. The limits of automatic summarisation according to ROUGE. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics. Association for Computational Linguistics, 41–45. DOI:

[50]

Abigail See, Peter J. Liu, and Christopher D. Manning. 2017. Get to the point: Summarization with pointer-generator networks. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 1073–1083. DOI:

[51]

Hongya Song, Zhaochun Ren, Shangsong Liang, Piji Li, Jun Ma, and Maarten de Rijke. 2017. Summarizing answers in non-factoid community question-answering. In Proceedings of the 10th ACM International Conference on Web Search and Data Mining. ACM, 405–414. DOI:

Digital Library

[52]

Ilya Sutskever, Oriol Vinyals, and Quoc V. Le. 2014. Sequence to sequence learning with neural networks. In Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014. 3104–3112. Retrieved from https://proceedings.neurips.cc/paper/2014/hash/a14ac55a4f27472c5d894ec1c3c743d2-Abstract.html.

[53]

Justine Raju Thomas, Santosh Kumar Bharti, and Korra Sathya Babu. 2016. Automatic keyword extraction for text summarization in e-newspapers. In Proceedings of the International Conference on Informatics and Analytics. ACM, 86:1–86:8. DOI:

Digital Library

[54]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Ł. ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Proceedings of the Advances in Neural Information Processing Systems. 5998–6008. Retrieved from http://papers.nips.cc/paper/7181-attention-is-all-you-need.pdf.

[55]

Jiuniu Wang, Wenjia Xu, Xingyu Fu, Yang Wei, Li Jin, Ziyan Chen, Guangluan Xu, and Yirong Wu. 2020. SRQA: Synthetic reader for factoid question answering. Knowledge-Based Systems 193 (2020), 105415. DOI:

Digital Library

[56]

Qianlong Wang and Jiangtao Ren. 2021. Summary-aware attention for social media short text abstractive summarization. Neurocomputing 425 (2021), 290–299. DOI:

[57]

Shuohang Wang and Jing Jiang. 2017. A compare-aggregate model for matching text sequences. In Proceedings of the 5th International Conference on Learning Representations.OpenReview.net. Retrieved from https://openreview.net/forum?id=HJTzHtqee.

[58]

Wai Ming Wang, Zhi Li, J. W. Wang, and Z. H. Zheng. 2017. How far we can go with extractive text summarization? Heuristic methods to obtain near upper bounds. Expert Systems with Applications 90 (2017), 439–463. DOI:

Digital Library

[59]

Xianzhi Wang, Chaoran Huang, Lina Yao, Boualem Benatallah, and Manqing Dong. 2018. A survey on expert recommendation in community question answering. Journal of Computer Science and Technology 33, 4 (2018), 625–653. DOI:

[60]

Bowen Xu, Zhenchang Xing, Xin Xia, David Lo, and Shanping Li. 2018. Domain-specific cross-language relevant question retrieval. Empirical Software Engineering 23, 2 (2018), 1084–1122. DOI:

Digital Library

[61]

Shuo Yang, Lei Zou, Zhongyuan Wang, Jun Yan, and Ji-Rong Wen. 2017. Efficiently answering technical questions - a knowledge graph approach. In Proceedings of the 31st AAAI Conference on Artificial Intelligence. AAAI, 3111–3118. Retrieved from http://aaai.org/ocs/index.php/AAAI/AAAI17/paper/view/14576.

[62]

YueLiu, Aihua Tang, Fei Cai, Pengfei Ren, and Zhibin Sun. 2019. Multi-feature based question-answerer model matching for predicting response time in CQA. Knowledge-Based Systems 182 (2019), 104794. DOI:

Digital Library

[63]

Evi Yulianti, Ruey-Cheng Chen, Falk Scholer, W. Bruce Croft, and Mark Sanderson. 2018. Document summarization for answering non-factoid queries. IEEE Transactions on Knowledge and Data Engineering 30, 1 (2018), 15–28. DOI:

Cited By

Menta AGarcia-Serrano A(2024)Reaching quality and efficiency with a parameter-efficient controllable sentence simplification approachComputer Science and Information Systems10.2298/CSIS230912017M21:3(899-921)Online publication date: 2024
https://doi.org/10.2298/CSIS230912017M
Li BYang PSun YHu ZYi M(2024)Advances and challenges in artificial intelligence text generation人工智能文本生成的进展与挑战Frontiers of Information Technology & Electronic Engineering10.1631/FITEE.230041025:1(64-83)Online publication date: 8-Feb-2024
https://doi.org/10.1631/FITEE.2300410
Wang YZhou LWang YPeng Z(2024)Leveraging Pretrained Language Models for Enhanced Entity MatchingInternational Journal of Intelligent Systems10.1155/2024/19412212024Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1155/2024/1941221
Show More Cited By

Index Terms

Hierarchical Sliding Inference Generator for Question-driven Abstractive Answer Summarization
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Natural language generation
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Question answering
      2. Summarization

Recommendations

Abstractive summarization: An overview of the state of the art
Highlights
- AMR Graphs are based upon PropBanks which limits them.
- Deep Learning Models ...
Abstract
Summarization, is to reduce the size of the document while preserving the meaning, is one of the most researched areas among the Natural Language Processing (NLP) community. Summarization techniques, on the basis of whether the exact ...
Graph-based abstractive biomedical text summarization
Graphical abstract

Display Omitted
Highlights
- A graph generation and frequent itemset mining approach have been used for the generation of extractive summaries.
- The T5 model has been adopted to generate abstractive summaries in the biomedical domain.
- The ROUGE metric has been ...
Abstract
Summarization is the process of compressing a text to obtain its important informative parts. In recent years, various methods have been presented to extract important parts of textual documents to present them in a summarized form. The first ...
Abstractive Summarization Improved by WordNet-Based Extractive Sentences
Natural Language Processing and Chinese Computing
Abstract
Recently, the seq2seq abstractive summarization models have achieved good results on the CNN/Daily Mail dataset. Still, how to improve abstractive methods with extractive methods is a good research direction, since extractive methods have their ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 41, Issue 1

January 2023

759 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3570137

Editor:
Min Zhang
Tsinghua University, China

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 January 2023

Online AM: 14 February 2022

Accepted: 17 January 2022

Revised: 02 November 2021

Received: 16 April 2021

Published in TOIS Volume 41, Issue 1

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Refereed

Funding Sources

Consulting Project of Chinese Academy of Engineering
Fundamental Research Funds for the Central Universities, the Collaborative Innovation Center of Novel Software Technology and Industrialization

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

32
Total Citations
View Citations
682
Total Downloads

Downloads (Last 12 months)246
Downloads (Last 6 weeks)9

Reflects downloads up to 04 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

Menta AGarcia-Serrano A(2024)Reaching quality and efficiency with a parameter-efficient controllable sentence simplification approachComputer Science and Information Systems10.2298/CSIS230912017M21:3(899-921)Online publication date: 2024
https://doi.org/10.2298/CSIS230912017M
Li BYang PSun YHu ZYi M(2024)Advances and challenges in artificial intelligence text generation人工智能文本生成的进展与挑战Frontiers of Information Technology & Electronic Engineering10.1631/FITEE.230041025:1(64-83)Online publication date: 8-Feb-2024
https://doi.org/10.1631/FITEE.2300410
Wang YZhou LWang YPeng Z(2024)Leveraging Pretrained Language Models for Enhanced Entity MatchingInternational Journal of Intelligent Systems10.1155/2024/19412212024Online publication date: 15-Apr-2024
https://dl.acm.org/doi/10.1155/2024/1941221
Breve BCimino GDeufemia V(2024)Hybrid Prompt Learning for Generating Justifications of Security Risks in Automation RulesACM Transactions on Intelligent Systems and Technology10.1145/3675401Online publication date: 29-Jun-2024
https://dl.acm.org/doi/10.1145/3675401
Jiang JGuo PXu XWu JCui Y(2024)Social Perception with Graph Attention Network for RecommendationACM Transactions on Recommender Systems10.1145/3665503Online publication date: 21-May-2024
https://dl.acm.org/doi/10.1145/3665503
Tang XChen LShi HLyu D(2024)DHyper: A Recurrent Dual Hypergraph Neural Network for Event Prediction in Temporal Knowledge GraphsACM Transactions on Information Systems10.1145/365301542:5(1-23)Online publication date: 29-Apr-2024
https://dl.acm.org/doi/10.1145/3653015
Katz OBarkan OKoenigstein N(2024)Personalized Cadence Awareness for Next Basket RecommendationACM Transactions on Recommender Systems10.1145/3652863Online publication date: 21-Mar-2024
https://dl.acm.org/doi/10.1145/3652863
Rong HQian MMa TJin DSheng V(2024)CoBjeason: Reasoning Covered Object in Image by Multi-Agent Collaboration Based on Informed Knowledge GraphACM Transactions on Knowledge Discovery from Data10.1145/364356518:5(1-56)Online publication date: 28-Feb-2024
https://dl.acm.org/doi/10.1145/3643565
Liu HGuo LZhu LJiang YGao MYin H(2024)MCRPL: A Pretrain, Prompt, and Fine-tune Paradigm for Non-overlapping Many-to-one Cross-domain RecommendationACM Transactions on Information Systems10.1145/364186042:4(1-24)Online publication date: 9-Feb-2024
https://dl.acm.org/doi/10.1145/3641860
Anand ALeonhardt JSingh JRudra KAnand A(2024)Data Augmentation for Sample Efficient and Robust Document RankingACM Transactions on Information Systems10.1145/363491142:5(1-29)Online publication date: 29-Apr-2024
https://dl.acm.org/doi/10.1145/3634911
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Full Text

View this article in Full Text.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View full text|Download PDF

View Issue’s Table of Contents