SupMMD: A Sentence Importance Model for Extractive Summarization using Maximum Mean Discrepancy (2020.findings-emnlp)
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| Challenge: | Existing work on multi-document summarization has focused on generic summarizing of information present in each document set. |
| Approach: | They propose a technique for generic and update summarization based on kernel two-sample testing. |
| Outcome: | The proposed technique exceeds the current state-of-the-art on two datasets. |
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Chao Zhao, Tenghao Huang, Somnath Basu Roy Chowdhury, Muthu Kumar Chandrasekaran, Kathleen McKeown, Snigdha Chaturvedi
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| Challenge: | Unsupervised approaches to extractive summarization rely on notion of sentence importance defined by semantic similarity between a sentence and the document. |
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SumPubMed: Summarization Dataset of PubMed Scientific Articles (2021.acl-srw)
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| Challenge: | Existing summarization models that can extract the top few lines of news articles fail to summarize long documents. |
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| Challenge: | Multi-document summarization (MDS) of news articles has been limited to datasets of a couple of hundred examples. |
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| Challenge: | Existing methods for extractive and abstractive summarization use token-level or sentence-level training objectives. |
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| Challenge: | Document-level neural machine translation (DocNMT) models can be difficult and expensive to train due to data sparsity. |
| Approach: | They propose an Importance-Aware Data Augmentation algorithm that augments training data based on token importance information estimated by the norm of hidden states and training gradients. |
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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. |
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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. |
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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 . |
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PreSumm: Predicting Summarization Performance Without Summarizing (2025.findings-acl)
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| Challenge: | Recent advances in summarization models do not produce all documents in the same way, despite their inherent design principles and operational mechanisms. |
| Approach: | They propose a task where a system predicts summarization performance based solely on the source document. |
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