Papers with Multi-News
Multi-News+: Cost-efficient Dataset Cleansing via LLM-based Data Annotation (2024.emnlp-main)
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| Challenge: | Various attempts to correct noisy data in the construction process have been made, but human annotation is expensive and time-consuming. |
| Approach: | They propose to use large language models for data annotation to imitate human annotation and classify unrelated documents from a multi-document summarization task. |
| Outcome: | The proposed method imitates human annotation and classifies unrelated documents from the Multi-News dataset. |
An Empirical Study on Topic Preservation in Multi-Document Summarization (2022.aacl-srw)
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| Challenge: | Multi-document summarization is a process of generating an informative and concise summary from multiple topic-related documents. |
| Approach: | They perform empirical analysis on two MDS datasets and study topic preservation on generated summaries from 8 MDS models. |
| Outcome: | The results show that extractive and abstractive summarization methods preserve topic information from source documents. |
Multi-News: A Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model (P19-1)
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| Challenge: | Multi-document summarization (MDS) of news articles has been limited to datasets of a couple of hundred examples. |
| Approach: | They propose a model which integrates a traditional extractive summarization model with a standard SDS model and achieves competitive results on MDS datasets. |
| Outcome: | The proposed model achieves competitive results on large-scale datasets. |
Improving Multi-Document Summarization through Referenced Flexible Extraction with Credit-Awareness (2022.naacl-main)
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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 . |
| Outcome: | The proposed framework outperforms baseline models with comparable model sizes and achieves the best results on the Multi-News, Multi-XScience, and WikiCatSum corpora. |
Topic-Guided Abstractive Multi-Document Summarization (2021.findings-emnlp)
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| Challenge: | Existing studies on multi-document summarization (MDS) focus on extractive and abstractive approaches to create a fluent and concise summary for a collection of thematically related documents. |
| Approach: | They propose a novel abstractive MDS model that represents multiple documents as a heterogeneous graph and then applies a graph-to-sequence framework to generate summaries. |
| Outcome: | The proposed model outperforms state-of-the-art models on Rouge scores and human evaluation, while learning high-quality topics. |
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. |
Event Graph based Sentence Fusion (2021.emnlp-main)
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| Challenge: | Sentence fusion is a conditional generation task that merges related sentences into a coherent text. |
| Approach: | They propose to build an event graph from the input sentences to capture related events in a structured way and use the constructed event graph to guide sentence fusion. |
| Outcome: | The proposed method achieves state-of-the-art on two datasets . it is based on the input sentences and shows that it is effective . |
A Multi-Document Coverage Reward for RELAXed Multi-Document Summarization (2022.acl-long)
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| Challenge: | Multi-document summarization models are limited by limited references and with maximum-likelihood objectives. |
| Approach: | They propose to fine-tune an MDS baseline with a reward that balances a reference-based metric such as ROUGE with coverage of the input documents. |
| Outcome: | The proposed model improves on the Multi-News and WCEP datasets with a low-variance estimator . the proposed model also improves the coverage of the input documents . |
Efficiently Summarizing Text and Graph Encodings of Multi-Document Clusters (2021.naacl-main)
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| Challenge: | Abstractive multi-document summarization (MDS) is a task that has seen advances with the introduction of large-scale datasets and powerful Transformer-based models. |
| Approach: | They propose an efficient graph-enhanced approach to multi-document summarization with an encoder-decoder Transformer model. |
| Outcome: | The proposed model scales to large input documents and improves on a multi-document dataset. |
Highlight-Transformer: Leveraging Key Phrase Aware Attention to Improve Abstractive Multi-Document Summarization (2021.findings-acl)
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| Challenge: | Existing models do not consider key phrases in determining attention weights of self-attention . Existing work does not consider the importance of key phrases when determining weights . |
| Approach: | They propose a model with highlighting mechanism to assign greater attention weights to key phrases . they propose two structures of highlighting attention for each head and the multihead highlighting . experimental results show that their proposed model significantly outperforms the baseline model . |
| Outcome: | The proposed model outperforms the baseline models on a multi-news dataset. |
Multi-Granularity Interaction Network for Extractive and Abstractive Multi-Document Summarization (2020.acl-main)
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| Challenge: | Existing methods for document summarization use extractive and abstractive representations, but they don't take into account hierarchical structure of document clusters. |
| Approach: | They propose a multi-granularity interaction network for extractive and abstractive multi-document summarization which jointly learn semantic representations for words, sentences, and documents. |
| Outcome: | The proposed model outperforms baseline methods and achieves the best results on the Multi-News dataset. |
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. |