Papers with multi-document setting
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. |
Abstractive Unsupervised Multi-Document Summarization using Paraphrastic Sentence Fusion (C18-1)
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| Challenge: | a new method for abstractive summarization is being developed for document summarizing . abstractive methods require extensive natural language generation to rewrite the sentences . |
| Approach: | They propose an unsupervised abstractive summarization system in multi-document context . they use a paraphrastic sentence fusion model which performs sentence synthesis and paraphrazing . |
| Outcome: | The proposed model improves information coverage and abstractiveness of generated sentences. |
Multi-Review Fusion-in-Context (2024.findings-naacl)
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| Challenge: | Current methods for generating text are opaque and difficult to control and interpret due to their opaque nature. |
| Approach: | They propose a modular approach with separate components for each step . they formalize Fusion-in-Context as a standalone task, whose input consists of source texts with highlighted spans of targeted content. |
| Outcome: | The proposed approach is based on a curated dataset of 1000 instances in the reviews domain and a novel evaluation framework for assessing the faithfulness and coverage of highlights. |
CDLM: Cross-Document Language Modeling (2021.findings-emnlp)
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| Challenge: | Existing language models (LMs) provide powerful representations for internal text structure, but there are important applications for multi-text tasks. |
| Approach: | They propose a pretraining approach that incorporates two key ideas into the masked language modeling objective. |
| Outcome: | The proposed model improves over existing models and sets of long-range transformers and can be easily applied to multiple multi-text tasks. |
MDBench: A Synthetic Multi-Document Reasoning Benchmark Generated with Knowledge Guidance (2025.findings-acl)
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| Challenge: | Multi-document reasoning is an area of increasing relevance given LLM capabilities in handling longer-context inputs, but few benchmarks exist to rigorously examine model behavior in this setting. |
| Approach: | They propose a new dataset for evaluating LLMs on the task of multi-document reasoning that uses condensed structured seed knowledge to modify it through LLM-assisted edits. |
| Outcome: | The proposed method generates document sets and QA examples on a multi-document reasoning task using a synthetic generation process. |