Countering the Effects of Lead Bias in News Summarization via Multi-Stage Training and Auxiliary Losses (D19-1)
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| Challenge: | Sentence position is a strong feature for news summarization, since the lead often summarizes the key points of the article. |
| Approach: | They propose two techniques to make neural systems sensitive to the importance of content in different parts of the article by using random shuffled sentences to pretrain the model. |
| Outcome: | The proposed techniques improve the performance of a competitive reinforcement learning based extractive system, with the auxiliary loss being more powerful than pretraining. |
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Demoting the Lead Bias in News Summarization via Alternating Adversarial Learning (2021.acl-short)
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| Challenge: | In news articles the lead bias dominates the learning signals for neural extractive summarizations, severely limiting their performance on data with different or even no bias. |
| Approach: | They propose a method to demote the lead bias in news and make the model focus more on the content semantics. |
| Outcome: | The proposed method can demote the model’s learned lead bias and improve its generality on out-of-distribution data with little to no performance loss on in-difference data. |
Earlier Isn’t Always Better: Sub-aspect Analysis on Corpus and System Biases in Summarization (D19-1)
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| Challenge: | despite recent advances in neural summarization systems, the underlying logic behind the improvements remains unexplored. |
| Approach: | They define three sub-aspects of summarization: position, importance, diversity . position exhibits substantial bias in news articles, but not with academic papers . |
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Ranking Sentences for Extractive Summarization with Reinforcement Learning (N18-1)
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| Challenge: | Abstractive summarization involves various text rewriting operations and has been identified as a sequence-to-sequence problem. |
| Approach: | They propose a novel algorithm which globally optimizes the ROUGE evaluation metric through a reinforcement learning objective. |
| Outcome: | The proposed algorithm outperforms state-of-the-art extractive and abstractive systems when evaluated automatically and by humans. |
Improving Multi-Document Summarization through Referenced Flexible Extraction with Credit-Awareness (2022.naacl-main)
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| Challenge: | Existing approaches to Multi-document summarization are limited due to the extremely long input length. |
| Approach: | They propose an extract-then-abstract Transformer framework to overcome the problem . they leverage pre-trained language models to construct hierarchical extractors and abstractors . |
| Outcome: | The proposed framework outperforms baseline models with comparable model sizes and achieves the best results on the Multi-News, Multi-XScience, and WikiCatSum corpora. |
Read Top News First: A Document Reordering Approach for Multi-Document News Summarization (2022.findings-acl)
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Chao Zhao, Tenghao Huang, Somnath Basu Roy Chowdhury, Muthu Kumar Chandrasekaran, Kathleen McKeown, Snigdha Chaturvedi
| Challenge: | Existing methods for extracting multi-document news summarization neglect relative importance of documents. |
| Approach: | They propose to concatenate all documents into a single meta-document and then summarize it using an SDS model. |
| Outcome: | The proposed approach outperforms state-of-the-art methods with more complex architectures. |
Improving the Robustness of Summarization Systems with Dual Augmentation (2023.acl-long)
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| Challenge: | Experimental results show that state-of-the-art summarization models have a significant decrease in performance on adversarial and noisy test sets. |
| Approach: | They propose a SummAttacker approach to generate adversarial samples based on pre-trained language models that can generate word-level synonym substitution and noise. |
| Outcome: | The proposed model performs better on noisy, attacked, and clean datasets than baseline models and is more robust on noisy and attacked datasets. |
Conditional Neural Generation using Sub-Aspect Functions for Extractive News Summarization (2020.findings-emnlp)
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| Challenge: | Recent advances in text summarization have overcome position bias in news articles . however, there are long-standing, unresolved challenges in extractive summarizing . |
| Approach: | They propose a neural framework that can flexibly control summary generation by introducing a set of sub-aspect functions. |
| Outcome: | The proposed framework can flexibly control summary generation by introducing sub-aspect functions . extracted summaries with minimal position bias are comparable with standard models . |
Summary Level Training of Sentence Rewriting for Abstractive Summarization (D19-54)
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| Challenge: | Existing models rely on sentence-level rewards or suboptimal labels to achieve summary-level ROUGE scores. |
| Approach: | They propose a model that extracts salient sentences from a document and paraphrases them to generate a summary. |
| Outcome: | The proposed model improves on CNN/Daily Mail and New York Times datasets. |
Deep Differential Amplifier for Extractive Summarization (2021.acl-long)
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| Challenge: | Existing approaches to extract summary from document with a disproportionate ratio of selected and unselected sentences are far from human performance. |
| Approach: | They propose a model that rebalances sentence-level extractive summarization by amplifying the semantic difference between each sentence and all other sentences and applying the residual unit as the second item of the differential amplifier to deepen the architecture. |
| Outcome: | The proposed model performs competitively against state-of-the-art methods on two benchmark datasets. |
Unsupervised Extractive Summarization by Pre-training Hierarchical Transformers (2020.findings-emnlp)
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| Challenge: | Existing methods for document summarization use graphs and unlabeled documents . Existing models require labeled data, and it is expensive to create summarized documents. |
| Approach: | They propose to rank sentences using transformer attentions and pre-training objectives by unlabeled documents. |
| Outcome: | The proposed model achieves state-of-the-art on unsupervised summarization and is less dependent on sentence positions. |