SQuALITY: Building a Long-Document Summarization Dataset the Hard Way (2022.emnlp-main)
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| Challenge: | Existing summarization datasets often have issues that seriously limit their usability. |
| Approach: | They propose a faster but more straightforward approach to developing summarization benchmark data . they use a protocol that hires highly-qualified contractors to read stories and write original summaries from scratch . |
| Outcome: | The proposed protocol is faster but more straightforward than scraping summaries from everyday text. |
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How well do you know your summarization datasets? (2021.findings-acl)
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| Challenge: | State-of-the-art summarization systems are trained on massive datasets scraped from the web. |
| Approach: | They manually analyse 600 samples from three popular summarization datasets . they use a six-class typology which captures different noise types and degrees of summarizing difficulty. |
| Outcome: | The proposed model performs better on large datasets than on the current models. |
BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization (P19-1)
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| Challenge: | Existing text summarization datasets are compiled from news articles, where summary-worthy content often appears in the beginning of input articles. |
| Approach: | They present a novel dataset, BIGPATENT, consisting of 1.3 million records of U.S. patent documents along with human written abstractive summaries. |
| Outcome: | The proposed dataset is compared with existing summarization datasets and demonstrates that salient content is evenly distributed in the input. |
What Have We Achieved on Text Summarization? (2020.emnlp-main)
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| Challenge: | Existing methods for text summarization have been investigated, but there are still gaps between them and human professionals. |
| Approach: | They analyze 8 major sources of errors on 10 representative summarization models manually. |
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Beyond Generic Summarization: A Multi-faceted Hierarchical Summarization Corpus of Large Heterogeneous Data (L18-1)
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| 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. |
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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. |
WikiSum: Coherent Summarization Dataset for Efficient Human-Evaluation (2021.acl-short)
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| Challenge: | Existing summarization datasets are limited in their ability to evaluate output . a human evaluation is necessary to understand and improve summarizing systems . |
| Approach: | They propose a dataset based on how-to articles and coherent paragraph summaries written in plain language. |
| Outcome: | The proposed dataset makes human evaluation easier and more effective . the authors compare the proposed dataset to existing ones on PubMed and the literature. |
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. |
Long Text and Multi-Table Summarization: Dataset and Method (2022.findings-emnlp)
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| Challenge: | Existing document summarization methods focus on the text and filter out the non-textual content. Existing methods cannot meet the requirements of summarizing long text and multiple tables in each report. |
| Approach: | They propose a dataset for automatic document summarization that uses text and tabular data to produce a concise summary covering the input document's salient information. |
| Outcome: | The proposed method can produce a concise summary covering the input document's salient information. |
Open Domain Multi-document Summarization: A Comprehensive Study of Model Brittleness under Retrieval (2023.findings-emnlp)
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| Challenge: | Multi-document summarization (MDS) assumes a set of topic-related documents is provided as input. |
| Approach: | They formalize the task and bootstrap it using existing datasets, retrievers and summarizers. |
| Outcome: | The proposed method reduces the sensitivity of summarizers to imperfect retrieval, but is highly sensitive to other errors. |
USB: A Unified Summarization Benchmark Across Tasks and Domains (2023.findings-emnlp)
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| Challenge: | Existing summarization benchmarks lack the rich annotations needed to address important problems related to control and reliability. |
| Approach: | They propose a Wikipedia-derived summarization benchmark with crowd-sourced annotations . they find that fine-tuned models outperform larger few-shot prompted language models . |
| Outcome: | The proposed model outperforms many-shot prompted language models on multiple tasks . the proposed model is based on Wikipedia annotations and can be used in other domains . |