Papers with BillSum
BillSum: A Corpus for Automatic Summarization of US Legislation (D19-54)
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| Challenge: | In the US Congress, over 10,000 bills are introduced each year, with state legislatures introducing tens of thousands of bills. |
| Approach: | They introduce the first dataset for summarizing US Congressional and California state bills . they demonstrate that models built on Congressional bills can be used to summarize California billa . |
| Outcome: | The proposed summarization methods can be applied to states without human-written summaries. |
OTExtSum: Extractive Text Summarisation with Optimal Transport (2022.findings-naacl)
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| Challenge: | Extractive text summarisation aims to select salient sentences from a document to form a short yet informative summary. |
| Approach: | They propose to formulate extractive text summarisation as an Optimal Transport (OT) problem and use it to obtain an optimal summary that minimises the transportation cost to a given document. |
| Outcome: | The proposed method outperforms state-of-the-art methods and learning-based methods on multiNews, PubMed, BillSum, and CNN/DM datasets. |
Planning with Learned Entity Prompts for Abstractive Summarization (2021.tacl-1)
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| Challenge: | a simple but flexible mechanism is used to ground the generation of abstractive summaries. |
| Approach: | They propose a mechanism to learn an intermediate plan to ground the generation of abstractive summaries. |
| Outcome: | The proposed model outperforms state-of-the-art methods for faithfulness on CNN and BillSum. |
An Efficient Coarse-to-Fine Facet-Aware Unsupervised Summarization Framework Based on Semantic Blocks (2022.coling-1)
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| Challenge: | Existing unsupervised summarization methods fail to consider efficiency and effectiveness when the input document is extremely long. |
| Approach: | They propose an efficient Coarse-to-Fine Facet-Aware Ranking framework for unsupervised long document summarization based on the semantic block. |
| Outcome: | The proposed framework can achieve new state-of-the-art unsupervised summarization results on Gov-Report, billSum, arXiv, and PubMed. |