Text Summarization with Pretrained Encoders
Yang Liu, Mirella Lapata. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
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Citation edge evidence
- Prefix-Tuning: Optimizing Continuous Prompts for Generation→Text Summarization with Pretrained EncodersLegacy edge — source detail unavailable
- Pre-trained models for natural language processing: A survey→Text Summarization with Pretrained EncodersLegacy edge — source detail unavailable
- Text Summarization with Pretrained Encoders→Exploiting Generative AI to Scale up Intelligent Tutoring SystemsLegacy edge — source detail unavailable
- Text Summarization with Pretrained Encoders→BERT: Pre-training of Deep Bidirectional Transformers for Language UnderstandingLegacy edge — source detail unavailable
- Text Summarization with Pretrained Encoders→ROUGE: A Package for Automatic Evaluation of SummariesLegacy edge — source detail unavailable
- Text Summarization with Pretrained Encoders→The Stanford CoreNLP Natural Language Processing ToolkitLegacy edge — source detail unavailable
- Text Summarization with Pretrained Encoders→Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Tr…Legacy edge — source detail unavailable
- Text Summarization with Pretrained Encoders→Get To The Point: Summarization with Pointer-Generator NetworksLegacy edge — source detail unavailable
- Text Summarization with Pretrained Encoders→Abstractive Text Summarization using Sequence-to-sequence RNNs and BeyondLegacy edge — source detail unavailable
- Text Summarization with Pretrained Encoders→The use of MMR, diversity-based reranking for reordering documents and producing summariesLegacy edge — source detail unavailable
- Text Summarization with Pretrained Encoders→Teaching Machines to Read and ComprehendLegacy edge — source detail unavailable
- Text Summarization with Pretrained Encoders→Teaching Machines to Read and ComprehendLegacy edge — source detail unavailable
- Text Summarization with Pretrained Encoders→Incorporating Copying Mechanism in Sequence-to-Sequence LearningLegacy edge — source detail unavailable
- Text Summarization with Pretrained Encoders→Neuromodulatory Control Networks (NCNs): A Biologically Inspired Architecture for Dynamic…Legacy edge — source detail unavailable
- Get To The Point: Summarization with Pointer-Generator Networks→Teaching Machines to Read and ComprehendLegacy edge — source detail unavailable
- Get To The Point: Summarization with Pointer-Generator Networks→ROUGE: A Package for Automatic Evaluation of SummariesLegacy edge — source detail unavailable
- Get To The Point: Summarization with Pointer-Generator Networks→Teaching Machines to Read and ComprehendLegacy edge — source detail unavailable
- Get To The Point: Summarization with Pointer-Generator Networks→Abstractive Text Summarization using Sequence-to-sequence RNNs and BeyondLegacy edge — source detail unavailable
- Get To The Point: Summarization with Pointer-Generator Networks→Incorporating Copying Mechanism in Sequence-to-Sequence LearningLegacy edge — source detail unavailable
- Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond→Teaching Machines to Read and ComprehendLegacy edge — source detail unavailable
- Incorporating Copying Mechanism in Sequence-to-Sequence Learning→ROUGE: A Package for Automatic Evaluation of SummariesLegacy edge — source detail unavailable
- Pre-trained models for natural language processing: A survey→BERT: Pre-training of Deep Bidirectional Transformers for Language UnderstandingLegacy edge — source detail unavailable
- Prefix-Tuning: Optimizing Continuous Prompts for Generation→ROUGE: A Package for Automatic Evaluation of SummariesLegacy edge — source detail unavailable
- Prefix-Tuning: Optimizing Continuous Prompts for Generation→BERT: Pre-training of Deep Bidirectional Transformers for Language UnderstandingLegacy edge — source detail unavailable
What this paper cites, inside the corpus
What cites it, inside the corpus
| Paper | Year | Cited |
|---|---|---|
| Prefix-Tuning: Optimizing Continuous Prompts for Generation | 2021 | 2,322 |
| Pre-trained models for natural language processing: A survey | 2020 | 1,521 |
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Topics
| Topic Modeling | Computer Science |
| Natural Language Processing Techniques | Computer Science |
| Computational and Text Analysis Methods | Social Sciences |
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