Papers with single-document summarization
Embrace Divergence for Richer Insights: A Multi-document Summarization Benchmark and a Case Study on Summarizing Diverse Information from News Articles (2024.naacl-long)
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Kung-Hsiang Huang, Philippe Laban, Alexander Fabbri, Prafulla Kumar Choubey, Shafiq Joty, Caiming Xiong, Chien-Sheng Wu
| Challenge: | Existing studies on multi-document summarization focus on collating information that all sources agree upon, but the task of summarizing diverse information remains underexplored. |
| Approach: | They propose a task of summarizing diverse information encountered in multiple news articles encompassing the same event using a dataset curated by a large language model. |
| Outcome: | The proposed task aims to summarize diverse information in multiple news articles encompassing the same event . the proposed task is difficult due to its limited coverage and verbosity biases . |
A Document-Level Text Simplification Dataset for Japanese (2024.lrec-main)
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| Challenge: | Document-level text simplification tasks combine summarization and intra-sentence simplification. |
| Approach: | They devised a Japanese document-level text simplification dataset based on newspaper articles and Wikipedia. |
| Outcome: | The proposed dataset compared Japanese document-level text simplification models with English models and newspaper articles. |
Multi-document Summarization with Maximal Marginal Relevance-guided Reinforcement Learning (2020.emnlp-main)
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| Challenge: | Recent studies on single-document summarization (SDS) benefit from advances in neural sequence learning, but they produce unsatisfactory results on multi-document summary (MDS). |
| Approach: | They propose a neural sequence learning method that unifies advanced neural SDS methods and statistical measures used in classical MDS. |
| Outcome: | The proposed method achieves state-of-the-art performance on benchmark MDS datasets. |
Abstractive Multi-Document Summarization via Joint Learning with Single-Document Summarization (2020.findings-emnlp)
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| Challenge: | Existing methods for document summarization are extractive and abstractive. |
| Approach: | They propose to jointly learn an abstractive single-document decoder and a decoding controller to aggregate the decoded outputs for multiple input documents. |
| Outcome: | The proposed model outperforms several baselines on two multi-document summarization datasets and proves that it is useful for both tasks. |
An Entity-Driven Framework for Abstractive Summarization (D19-1)
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| Challenge: | Popular neural summarization models produce incoherent and unfaithful summaries . however, their outputs are often incohérent and incoerent . |
| Approach: | They propose a system for ENtity-drivEn Coherent Abstractive summarization framework that leverages entity information to generate informative and coherent abstracts. |
| Outcome: | The proposed framework outperforms existing state-of-the-art models on New York Times and CNN/Daily Mail datasets. |
Neural Extractive Text Summarization with Syntactic Compression (D19-1)
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| Challenge: | Recent approaches to summarization are either selection-based extraction or generation-based abstraction. |
| Approach: | They propose a neural model for single-document summarization based on joint extraction and syntactic compression. |
| Outcome: | The proposed model outperforms an off-the-shelf compression module and its output generally remains grammatical. |
Adapting the Neural Encoder-Decoder Framework from Single to Multi-Document Summarization (D18-1)
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| Challenge: | Existing methods to summarize short texts using a neural encoder-decoder are limited and expensive to obtain. |
| Approach: | They propose to use a maximal marginal relevance method to select representative sentences from multi-document input and leverage an abstractive encoder-decoder model to fuse disparate sentences to an abstract. |
| Outcome: | The proposed method compares favorably to state-of-the-art extractive and abstractive approaches judged by automatic metrics and human assessors. |
Auto-hMDS: Automatic Construction of a Large Heterogeneous Multilingual Multi-Document Summarization Corpus (L18-1)
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| Challenge: | Existing datasets for automatic text summarization are small and focused on newswires. |
| Approach: | They propose to automatically generate a large multilingual multi-document summarization corpus using Wikipedia articles as summaries and to automatically search for appropriate source documents. |
| Outcome: | The proposed corpus contains 7,316 topics in English and German with different summary lengths and number of source documents. |
Topic-Guided Reinforcement Learning with LLMs for Enhancing Multi-Document Summarization (2025.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have shown impressive results in single-document summarization, but their performance on MDS still leaves room for improvement. |
| Approach: | They propose a topic-guided reinforcement learning approach to improve content selection in MDS . explicit prompting models with topic labels enhances the informativeness, they show . |
| Outcome: | The proposed method outperforms baselines on multi-News and multi-XScience datasets. |
A Summarization Dataset of Slovak News Articles (2020.lrec-1)
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| Challenge: | a number of studies on document summarization have focused on the English language . however, most of the work on this task is done on English datasets . |
| Approach: | They propose to use a news site's ROUGE metric to adapt it to Slovak texts . they propose to introduce a large-scale news-based summarization dataset . |
| Outcome: | The proposed approach is better suited for Slovak texts than the dominant ROUGE metric. |