| Challenge: | Abstractive summarization models with maximum likelihood estimation generate unfaithful facts alongside ambiguous focus. |
| Approach: | They propose a framework which learns a regular summarization model to mimic the behavior of being guided by prophecy for boosting abstractive summaries. |
| Outcome: | The proposed model achieves new or matched state-of-the-art on four well-known datasets. |
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GSum: A General Framework for Guided Neural Abstractive Summarization (2021.naacl-main)
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| Challenge: | Abstractive summarization models are flexible, but they can be difficult to control. |
| Approach: | They propose a general and extensible guided summarization framework that takes different kinds of guidance as input and perform experiments across different varieties. |
| Outcome: | The proposed framework can generate more faithful summaries and different types of guidance generate qualitatively different summary. |
Noisy Self-Knowledge Distillation for Text Summarization (2021.naacl-main)
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| Challenge: | Existing approaches to summarize text using a single reference and noisy datasets are ill-suited to summarising on single reference datasets. |
| Approach: | They propose to use self-knowledge distillation to improve text summarization by generating smoothed labels for students and teachers to reduce model uncertainty. |
| Outcome: | The proposed framework improves on pretrained and non-pretrained models on three benchmarks. |
Planning with Learned Entity Prompts for Abstractive Summarization (2021.tacl-1)
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| Challenge: | a simple but flexible mechanism is used to ground the generation of abstractive summaries. |
| Approach: | They propose a mechanism to learn an intermediate plan to ground the generation of abstractive summaries. |
| Outcome: | The proposed model outperforms state-of-the-art methods for faithfulness on CNN and BillSum. |
Jointly Learning Guidance Induction and Faithful Summary Generation via Conditional Variational Autoencoders (2022.findings-naacl)
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| Challenge: | Existing methods for abstractive summarization generate factual consistency summaries with a high level of accuracy and coherence. |
| Approach: | They propose a framework that induces the guidance information and generates summary equipment with the guidance synchronously. |
| Outcome: | The proposed framework generates fluent summaries with no constraint on the words and phrases, and is more faithful than the existing state-of-the-art approaches. |
BRIO: Bringing Order to Abstractive Summarization (2022.acl-long)
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| Challenge: | Abstractive summarization models are often trained with maximum likelihood estimation (MLE) . mLE assumes a deterministic (one-point) target distribution, but can cause performance degradation . |
| Approach: | They propose a new training paradigm which assumes a non-deterministic distribution so that different candidate summaries are assigned probability mass according to their quality. |
| Outcome: | The proposed model can estimate probabilities of candidate summaries that are more correlated with their level of quality. |
Enhancing Abstractiveness of Summarization Models through Calibrated Distillation (2023.findings-emnlp)
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| Challenge: | Existing methods to generate abstractive summarizations are slow and abstractive, but we propose a novel approach to enhance the level of abstractiveness without sacrificing the informativeness of generated summaries. |
| Approach: | They propose a novel approach to enhance the level of abstractiveness without sacrificing the informativeness of generated summaries by exposing diverse pseudo summary with two supervision to the student model. |
| Outcome: | The proposed method outperforms previous methods in abstractive summarization distillation, producing highly abstractive and informative summaries. |
Alleviating Exposure Bias in Abstractive Summarization via Sequentially Generating and Revising (2024.lrec-main)
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| Challenge: | Existing approaches to abstractive summarization suffer from exposure bias . Existing solutions bridge this gap through un- or semi-supervised holistic learning . |
| Approach: | They propose to reformat abstractive summarization to sequential generation and revision (SeGRe) this allows the model to assess the flawed summary from a global perspective and modify inappropriate expressions. |
| Outcome: | The proposed model can assess the flawed summary from a global view and modify inappropriate expressions. |
Active Learning for Abstractive Text Summarization via LLM-Determined Curriculum and Certainty Gain Maximization (2024.findings-emnlp)
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| Challenge: | Abstractive text summarization (ATS) requires laborious data annotation and time-consuming model training. |
| Approach: | They propose a novel active learning framework that asks large language models to rate difficulty of instances and then uses certainty gain maximization to select instances with a distribution that aligns well with the overall distribution. |
| Outcome: | The proposed framework improves stability, effectiveness, and efficiency of abstractive text summarization backbones. |
Attention Temperature Matters in Abstractive Summarization Distillation (2022.acl-long)
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| Challenge: | Recent progress of abstractive text summarization relies on large pre-trained sequence-to-sequence Transformer models, which are computationally expensive. |
| Approach: | They propose to distill large Transformer summarization models into smaller ones with minimal performance loss by manipulating attention temperatures in Transformers. |
| Outcome: | The proposed method outperforms vanilla pseudo-labeling based methods on three summarization datasets and is shorter and more abstractive. |
Curriculum Prompt Learning with Self-Training for Abstractive Dialogue Summarization (2022.emnlp-main)
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| Challenge: | Existing methods to summarize dialogues are difficult due to insufficient training data and low information density. |
| Approach: | They propose a curriculum-based prompt learning method with self-training that gradually increases the degree of prompt perturbation, improving dialogue understanding and modeling capabilities. |
| Outcome: | The proposed model outperforms baseline models on the AMI and ICSI datasets and human evaluations show it is superior in the quality of the summary generation. |