Challenge: Automated summarization has focused on ten to twenty documents, typically news articles, but could in theory analyze hundreds of documents from a wide range of sources and provide an overview to the interested reader.
Approach: They propose a method for creating hierarchical summarization corpora from large, heterogeneous document collections by crowdsourcing relevant content and asking trained annotators to order the relevant information hierarchically.
Outcome: The proposed method can be used to develop and evaluate hierarchical summarization systems.

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Inducing Document Structure for Aspect-based Summarization (P19-1)

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Challenge: Abstractive summarization systems treat documents as unstructured and generate a single generic summary per document.
Approach: They propose to incorporate document structure into automatic summarization systems . they induce latent document structure and abstractive summarizing objective .
Outcome: The proposed model improves on topic-agnostic baselines and can produce abstractive and extractive aspect-based summaries.
Bringing Structure into Summaries: a Faceted Summarization Dataset for Long Scientific Documents (2021.acl-short)

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Challenge: Faceted summarization provides briefings of a document from different perspectives.
Approach: They propose a faceted summarization benchmark built on Emerald journal articles . they propose faceted models that bring structure into faceted documents .
Outcome: The proposed benchmark is based on Emerald journal articles and covers a diverse range of domains.
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.
Summarization Beyond News: The Automatically Acquired Fandom Corpora (2020.lrec-1)

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Challenge: Abstractive summarization methods require large corpora to train neural architectures.
Approach: They propose a novel automatic corpus construction approach that automatically constructs large open-licensed summarization corpora from existing large text collections and an evaluation process with human annotators.
Outcome: The proposed approach can be used to train abstractive summarization models on large corpora and through a manual evaluation with human annotators.
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.
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.
Outcome: The proposed model improves on the WikiSum dataset and can process multiple documents in a hierarchical manner.
A Repository of Corpora for Summarization (L18-1)

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Challenge: Summarization corpora are numerous but fragmented, making it difficult to pinpoint corporata best suited for a given summarization task.
Approach: They propose a repository containing corpora available to train and evaluate automatic summarization systems.
Outcome: The proposed system is based on a repository of corpora available for summarization tasks.
NEWTS: A Corpus for News Topic-Focused Summarization (2022.findings-acl)

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Challenge: Existing benchmarking corpora provide concordant pairs of full and abridged versions of Web, news or professional content.
Approach: They propose a topical summarization corpus called NEWTS that is annotated via crowd-sourcing.
Outcome: The proposed model can condition summaries on a desired range of themes . the proposed model outperforms Lead-3 baselines on most benchmark datasets .
Relational Summarization for Corpus Analysis (N18-1)

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Challenge: Existing methods for summarizing textual content are often ignored . relationshipal questions are ubiquitous and varied.
Approach: They propose a method which generates a natural language summary of the relationship between two lexical items in a corpus without reference to a knowledge base.
Outcome: The proposed method generates a natural language summary of the relationship between two lexical items in a corpus without reference to a knowledge base.
The State and Fate of Summarization Datasets: A Survey (2025.naacl-long)

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Challenge: Summarization is the task of shortening a text while preserving the most important information it contains.
Approach: They propose a novel ontology covering sample properties, collection methods and distribution covering sample characteristics, collection method and distribution.
Outcome: The proposed ontology covers sample properties, collection methods and distribution, and can be used to streamline future research into a more coherent body of work.

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