| Challenge: | In this paper, we examine the generalization behaviour of summarization models . we propose several properties of datasets that matter for generalization . |
| Approach: | They propose several properties of datasets which matter for generalization of summarization models. |
| Outcome: | The proposed approach improves the state-of-the-art model by rethinking the model design process on a typical dataset. |
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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. |
| Outcome: | The proposed framework achieves state-of-the-art on CNN/DailyMail by a large margin based on observations and analysis. |
How well do you know your summarization datasets? (2021.findings-acl)
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| Challenge: | State-of-the-art summarization systems are trained on massive datasets scraped from the web. |
| Approach: | They manually analyse 600 samples from three popular summarization datasets . they use a six-class typology which captures different noise types and degrees of summarizing difficulty. |
| Outcome: | The proposed model performs better on large datasets than on the current models. |
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. |
CDEvalSumm: An Empirical Study of Cross-Dataset Evaluation for Neural Summarization Systems (2020.findings-emnlp)
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| Challenge: | Existing evaluation methods for text summarization systems are limited to in-domain setting, where supervised pre-trained models are evaluated on the same dataset. |
| Approach: | They propose to use a cross-dataset evaluation approach to evaluate different summarization systems in a multi-domain setting. |
| Outcome: | The proposed model can be used to evaluate text summarization systems on different datasets. |
Bias in News Summarization: Measures, Pitfalls and Corpora (2024.findings-acl)
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| Challenge: | Pretrained large language models can reproduce harmful social biases in constrained settings, such as summarization. |
| Approach: | They propose a method to generate input documents with carefully controlled demographic attributes and then apply it to a controlled setting. |
| Outcome: | The proposed method allows to generate input documents with carefully controlled demographic attributes while working with real-world input documents. |
Understanding the Behaviour of Neural Abstractive Summarizers using Contrastive Examples (N19-1)
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| Challenge: | Neural abstractive summarization systems generate summary texts conditioned on the input source text, and have recently achieved high ROUGE scores on benchmark summarizing datasets. |
| Approach: | They propose to analyze existing neural abstractive summarization systems by comparing their performance to human-written summaries. |
| Outcome: | The proposed systems perform better than human-written summarizations on different datasets and show that they are able to understand deeper syntactic and semantic structures. |
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. |
Do Neural Language Models Overcome Reporting Bias? (2020.coling-main)
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| Challenge: | Recent studies show that pre-trained language models can overcome reporting bias by estimating the plausibility of rare but unspoken facts. |
| Approach: | They revisit the experiments conducted by Gordon and Van Durme (2013) . they find that pre-trained language models overestimate the very rare . |
| Outcome: | The proposed approach overestimates the rare at the expense of the rare, while minimizing reporting bias. |
Data Factors for Better Compositional Generalization (2023.emnlp-main)
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| Challenge: | Recent diagnostic datasets on compositional generalization expose severe problems . state-of-the-art models trained on larger and more general datasets show better generalization ability . |
| Approach: | They conduct an empirical analysis by training Transformer models on a variety of training sets with different data factors including dataset scale, pattern complexity, example difficulty, etc. |
| Outcome: | The proposed model training on larger datasets improves on compositional generalization tasks. |
Bias in Opinion Summarisation from Pre-training to Adaptation: A Case Study in Political Bias (2024.eacl-long)
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| Challenge: | Existing studies have focused on extractive summarisation but limited attention has been paid to abstractive summaries. |
| Approach: | They propose to trace bias in abstractive summarisation models to social media opinions using different models and adaptation methods. |
| Outcome: | The proposed model is compared with other models and adaptation methods to summarise social media opinions using different models and adaption methods. |