Papers with extractive strategies
Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization (D18-1)
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| Challenge: | Existing approaches to summarize documents are not extractive and require an abstractive approach. |
| Approach: | They propose a novel abstractive model which is conditioned on the article’s topics and based entirely on convolutional neural networks. |
| Outcome: | The proposed model outperforms an oracle extractive system and state-of-the-art abstractive approaches when evaluated automatically and by humans. |
Enhancing Pre-Trained Generative Language Models with Question Attended Span Extraction on Machine Reading Comprehension (2024.emnlp-main)
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| Challenge: | Extractive Machine Reading Comprehension (MRC) is a challenging field in the field of Natural Language Processing. |
| Approach: | They propose a Question-Attended Span Extraction module to address the limitations of generative approaches for extractive machine reading comprehension (MRC) . module significantly enhances performance of pre-trained generative language models, enabling them to surpass the extractive capabilities of advanced Large Language Models (LLMs) |
| Outcome: | The QASE module surpasses state-of-the-art models in few-shot settings. |
Multi-Fact Correction in Abstractive Text Summarization (2020.emnlp-main)
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| Challenge: | Existing abstractive summarization systems generate incorrect facts with respect to the source text. |
| Approach: | They propose a suite of two factual correction models that leverages question-answering knowledge to make corrections in system-generated summaries via span selection. |
| Outcome: | The proposed model improves factuality of news summarization without sacrificing summary quality. |