| Challenge: | Pre-trained language models have shown impressive results when fine-tuned on large summarization datasets. |
| Approach: | They analyze the training dynamics for generation models, focusing on summarization . they find that a propensity to copy the input is learned early in the training process . |
| Outcome: | The proposed model learns at different stages of fine-tuning, the authors show . they show that factual errors are learnt in later stages, but not at high-loss tokens . |
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Dissecting Generation Modes for Abstractive Summarization Models via Ablation and Attribution (2021.acl-long)
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| Challenge: | Abstractive summarization models have made great strides in recent years, but little is known about how they actually form summaries and how to understand where their decisions come from. |
| Approach: | They propose a two-step method to interpret summarization model decisions by categorizing each decoder decision into one of several generation modes. |
| Outcome: | The proposed method can identify phrases the summarization model has memorized and determine where in the training pipeline this memorization happened, and study complex generation phenomena on a per-instance basis. |
Annotating and Modeling Fine-grained Factuality in Summarization (2021.naacl-main)
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| Challenge: | Recent abstractive summarization systems produce factual errors that are not faithful to the input . current methods are lacking in identifying what errors are most important to target . |
| Approach: | They use synthetic and human-labeled data to identify factual errors in summarization and train models on the factuality detection task. |
| Outcome: | The proposed model detects factual errors on word, dependency, and sentence levels. |
Inference Time Style Control for Summarization (2021.naacl-main)
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| Challenge: | Existing methods to generate summaries of different styles without training separate models are lacking parallel data and expensive (re)training. |
| Approach: | They propose two methods that can be deployed during summary decoding on any pre-trained Transformer-based summarization model. |
| Outcome: | The proposed methods generate news headlines with various ideological leanings while still informative. |
Can LMs Generalize to Future Data? An Empirical Analysis on Text Summarization (2023.emnlp-main)
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| Challenge: | Existing summarization benchmarks overlap in time with pre-training corpora and fine-tuning datasets. |
| Approach: | They propose a temporal generalization benchmark that contains data samples from 2010 to 2022 to understand the temporal ability of abstractive summarization models. |
| Outcome: | The proposed benchmark analyzes data samples from 2010 to 2022 to understand the temporal generalization ability of abstractive summarization models. |
What Have We Achieved on Text Summarization? (2020.emnlp-main)
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| Challenge: | Existing methods for text summarization have been investigated, but there are still gaps between them and human professionals. |
| Approach: | They analyze 8 major sources of errors on 10 representative summarization models manually. |
| Outcome: | Aiming to gain more understanding of summarization systems with respect to their strengths and limitations on a fine-grained syntactic and semantic level, we use 8 major sources of errors on 10 representative summarizing models. |
Model Intrinsic Features of Fine-tuning based Text Summarization Models for Factual Consistency (2023.findings-acl)
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| Challenge: | a summarization model with relatively low factual consistency is more likely to model summaries that are not conditional to the documents. |
| Approach: | They analyze the model intrinsic features by varying the fine-tuning objectives and datasets. |
| Outcome: | The proposed models have a high inductive bias for factual consistency and are more likely to model summaries that are not conditional to the documents. |
Content Selection in Deep Learning Models of Summarization (D18-1)
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| Challenge: | Using deep learning models, we find that word embedding does not improve performance over simpler models. |
| Approach: | They propose to use sentence embedding to perform content selection across multiple domains . they propose to propose two alternative models that use auto-regressive sentence extraction . |
| Outcome: | The proposed models improve performance across news, personal stories, meetings, and medical articles. |
Correcting Diverse Factual Errors in Abstractive Summarization via Post-Editing and Language Model Infilling (2022.emnlp-main)
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| Challenge: | Abstractive summarization models often generate inconsistent summaries containing factual errors or fabricated content. |
| Approach: | They propose to generate representative examples of non-factual summaries through infilling language models and train a robust fact-correction model to post-edit them to improve factual consistency. |
| Outcome: | The proposed model outperforms previous methods in correcting factual errors on two popular summarization datasets. |
Searching for Effective Neural Extractive Summarization: What Works and What’s Next (P19-1)
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| Challenge: | Recent years have seen success in the use of deep neural networks on text summarization, but there is no clear understanding of why they perform so well or how they might be improved. |
| Approach: | They propose to use different types of model architectures to improve extractive summarization systems. |
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CONFIT: Toward Faithful Dialogue Summarization with Linguistically-Informed Contrastive Fine-tuning (2022.naacl-main)
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Xiangru Tang, Arjun Nair, Borui Wang, Bingyao Wang, Jai Desai, Aaron Wade, Haoran Li, Asli Celikyilmaz, Yashar Mehdad, Dragomir Radev
| Challenge: | Factual inconsistencies in generated summaries severely limit the practical applications of abstractive dialogue summarization. |
| Approach: | They propose a typology of factual errors to better understand hallucinations generated by current models and a contrastive fine-tuning strategy to improve the factual consistency and overall quality of summaries. |
| Outcome: | The proposed model significantly reduces all kinds of factual errors on both SAMSum dialogue summarization and AMI meeting summarizing datasets. |