Papers with abstractive summarization tasks
ExtraPhrase: Efficient Data Augmentation for Abstractive Summarization (2022.naacl-srw)
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| Challenge: | Recent studies indicated that neural methods are governed by the scaling law for the amount of training data. |
| Approach: | They propose a low-cost strategy to augment training data for abstractive summarization tasks by extracting summarized text and paraphrasing it. |
| Outcome: | The proposed strategy outperforms back-translation and self-training and is more cost-efficient when training data is small. |
Exploring Iterative Controllable Summarization with Large Language Models (2026.findings-eacl)
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| Challenge: | Large language models (LLMs) excel at abstractive summarization tasks, but their ability to precisely control summary attributes remains underexplored. |
| Approach: | They propose a guide-to-explain framework for controllable summarization that enables the model to identify misaligned attributes in the initial draft and guides it to self-explan errors in the previous output. |
| Outcome: | The proposed framework generates well-adjusted summaries that satisfy the desired attributes with robust effectiveness while requiring surprisingly fewer iterations than other iterative approaches. |
On Context Utilization in Summarization with Large Language Models (2024.acl-long)
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| Challenge: | Large language models excel in abstractive summarization tasks, delivering fluent and pertinent summaries. |
| Approach: | They conduct the first comprehensive study on context utilization and position bias in summarization. |
| Outcome: | The proposed benchmark compares two methods to alleviate position bias in summarization tasks. |
Sparsity and Sentence Structure in Encoder-Decoder Attention of Summarization Systems (2021.emnlp-main)
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| Challenge: | Training and inference using large transformer models can be computationally expensive because the self-attention's time and memory grow quadratically with sequence length. |
| Approach: | They propose a modified transformer architecture that constrains the encoder-decoder attention mechanism to a subset of input sentences while maintaining system performance. |
| Outcome: | The proposed architecture can be trained and inferenced using large transformer models with expensive training and induction costs. |