Challenge: Hierarchical merging is a technique used to summarize very long texts . it can amplify LLM hallucinations, increasing the risk of factual inaccuracies .
Approach: They propose to enrich hierarchical merging with context from the source document to reduce the risk of factual inaccuracies.
Outcome: The proposed methods outperform zero-shot and hierarchical merging baselines on legal and narrative datasets.

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Challenge: a recent study shows that multimodal summarization is not efficient for long inputs and outputs.
Approach: They extend a TV episode transcript summarization dataset and create a multimodal variant by collecting full-length videos.
Outcome: The proposed model can be tuned to perform multimodal summarization tasks efficiently using adapter modules augmented with a hierarchical structure while tuning only 3.8% of model parameters.
Extractive Summarization of Long Documents by Combining Global and Local Context (D19-1)

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Challenge: Existing methods for extractive and abstractive summarization are far from human performance.
Approach: They propose a neural single-document extractive summarization model for long documents that incorporates both the global context of the whole document and the local context.
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Exploiting Discourse-Level Segmentation for Extractive Summarization (D19-54)

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Challenge: Existing approaches to extract summarize text are based on sentences as the elementary unit, but semantic segments containing supplementary information or descriptive details are often nonessential in the generated summaries.
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Summarize, Outline, and Elaborate: Long-Text Generation via Hierarchical Supervision from Extractive Summaries (2022.coling-1)

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Challenge: Existing models focus on local word prediction, and cannot make high level plans on what to generate.
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Deep Differential Amplifier for Extractive Summarization (2021.acl-long)

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Challenge: Existing approaches to extract summary from document with a disproportionate ratio of selected and unselected sentences are far from human performance.
Approach: They propose a model that rebalances sentence-level extractive summarization by amplifying the semantic difference between each sentence and all other sentences and applying the residual unit as the second item of the differential amplifier to deepen the architecture.
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Hierarchical Transformers for Multi-Document Summarization (P19-1)

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Challenge: Existing models for multidocument summarization have been developed that can process multiple documents in a hierarchical manner.
Approach: They propose a neural summarization model which can process multiple input documents and distill Transformer architecture with the ability to encode documents in a hierarchical manner.
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Long Document Summarization with Top-down and Bottom-up Inference (2023.findings-eacl)

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Challenge: Recent models infer latent representations of words or tokens with a transformer encoder, which is bottom-up and thus does not capture long-distance context well.
Approach: They propose a method to infer latent representations of words or tokens in documents . they assume a hierarchical structure of a document where top-level captures long range dependency .
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NexusSum: Hierarchical LLM Agents for Long-Form Narrative Summarization (2025.acl-long)

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Challenge: Summarizing long-form narratives requires capturing intricate plotlines, character interactions, and thematic coherence over tens of thousands of tokens.
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Globalizing BERT-based Transformer Architectures for Long Document Summarization (2021.eacl-main)

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Challenge: Existing approaches to fine-tune a large language model on downstream tasks show several limitations when the target task requires to reason with long documents.
Approach: They propose a hierarchical approach where the input is divided in multiple blocks independently processed by the scaled dot-attentions and combined between the successive layers.
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On Context Utilization in Summarization with Large Language Models (2024.acl-long)

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Challenge: Large language models excel in abstractive summarization tasks, delivering fluent and pertinent summaries.
Approach: They conduct the first comprehensive study on context utilization and position bias in summarization.
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