# Bringing Structure into Summaries: a Faceted Summarization Dataset for Long Scientific Documents

arXiv: https://arxiv.org/abs/2106.00130 · HTML: https://mlsanity.com/paper/2106.00130/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 41 references that are in the map.
- First posted on arXiv 31 May 2021.

## Builds on (most cited first, 17 of 17)

- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization](https://mlsanity.com/paper/1808.08745/) (2018)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents](https://mlsanity.com/paper/1804.05685/) (2018)
- [A Deep Reinforced Model for Abstractive Summarization](https://mlsanity.com/paper/1705.04304/) (2017)
- [Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies](https://mlsanity.com/paper/1804.11283/) (2018)
- [SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents](https://mlsanity.com/paper/1611.04230/) (2016)
- [BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization](https://mlsanity.com/paper/1906.03741/) (2019)
- [TLDR: Extreme Summarization of Scientific Documents](https://mlsanity.com/paper/2004.15011/) (2020)
- [Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles](https://mlsanity.com/paper/2010.14235/) (2020)
- [On Extractive and Abstractive Neural Document Summarization with Transformer Language Models](https://mlsanity.com/paper/1909.03186/) (2019)
- [ScisummNet: A Large Annotated Corpus and Content-Impact Models for Scientific Paper Summarization with Citation Networks](https://mlsanity.com/paper/1909.01716/) (2019)
- [A Supervised Approach to Extractive Summarisation of Scientific Papers](https://mlsanity.com/paper/1706.03946/) (2017)
- [SEAL: Segment-wise Extractive-Abstractive Long-form Text Summarization](https://mlsanity.com/paper/2006.10213/) (2020)
- [Discourse-Aware Unsupervised Summarization of Long Scientific Documents](https://mlsanity.com/paper/2005.00513/) (2020)
- [Structured Summarization of Academic Publications](https://mlsanity.com/paper/1905.07695/) (2019)

## Built on by (most cited first, 30 of 33)

- [A Comprehensive Survey on Process-Oriented Automatic Text Summarization with Exploration of LLM-Based Methods](https://mlsanity.com/paper/2403.02901/) (2024)
- [An Empirical Survey on Long Document Summarization: Datasets, Models and Metrics](https://mlsanity.com/paper/2207.00939/) (2022)
- [SQuALITY: Building a Long-Document Summarization Dataset the Hard Way](https://mlsanity.com/paper/2205.11465/) (2022)
- [HIBRIDS: Attention with Hierarchical Biases for Structure-aware Long Document Summarization](https://mlsanity.com/paper/2203.10741/) (2022)
- [Automated scholarly paper review: Concepts, technologies, and challenges](https://mlsanity.com/paper/2111.07533/) (2021)
- [The ACL OCL Corpus: Advancing Open Science in Computational Linguistics](https://mlsanity.com/paper/2305.14996/) (2023)
- [Related Work and Citation Text Generation: A Survey](https://mlsanity.com/paper/2404.11588/) (2024)
- [Generating a Structured Summary of Numerous Academic Papers: Dataset and Method](https://mlsanity.com/paper/2302.04580/) (2023)
- [LimGen: Probing the LLMs for Generating Suggestive Limitations of Research Papers](https://mlsanity.com/paper/2403.15529/) (2024)
- [OpenAsp: A Benchmark for Multi-document Open Aspect-based Summarization](https://mlsanity.com/paper/2312.04440/) (2023)
- [A Comparative Study of Quality Evaluation Methods for Text Summarization](https://mlsanity.com/paper/2407.00747/) (2024)
- [X-SCITLDR: Cross-Lingual Extreme Summarization of Scholarly Documents](https://mlsanity.com/paper/2205.15051/) (2022)
- [LexAbSumm: Aspect-based Summarization of Legal Decisions](https://mlsanity.com/paper/2404.00594/) (2024)
- [From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systems](https://mlsanity.com/paper/2503.01424/) (2025)
- [HaRiM^+: Evaluating Summary Quality with Hallucination Risk](https://mlsanity.com/paper/2211.12118/) (2022)
- [HDT: Hierarchical Document Transformer](https://mlsanity.com/paper/2407.08330/) (2024)
- [Bridging Research and Readers: A Multi-Modal Automated Academic Papers Interpretation System](https://mlsanity.com/paper/2401.09150/) (2024)
- [Disordered-DABS: A Benchmark for Dynamic Aspect-Based Summarization in Disordered Texts](https://mlsanity.com/paper/2402.10554/) (2024)
- [Citance-Contextualized Summarization of Scientific Papers](https://mlsanity.com/paper/2311.02408/) (2023)
- [LCFO: Long Context and Long Form Output Dataset and Benchmarking](https://mlsanity.com/paper/2412.08268/) (2024)
- [AcademicEval: Live Long-Context LLM Benchmark](https://mlsanity.com/paper/2510.17725/) (2025)
- [Explaining Relationships Among Research Papers](https://mlsanity.com/paper/2402.13426/) (2024)
- [PerSEval: Assessing Personalization in Text Summarizers](https://mlsanity.com/paper/2407.00453/) (2024)
- [The State and Fate of Summarization Datasets: A Survey](https://mlsanity.com/paper/2411.04585/) (2024)
- [Summarization with Graphical Elements](https://mlsanity.com/paper/2204.07551/) (2022)
- [Good Data, Large Data, or No Data? Comparing Three Approaches in Developing Research Aspect Classifiers for Biomedical Papers](https://mlsanity.com/paper/2306.04820/) (2023)
- [Surveying the Landscape of Text Summarization with Deep Learning: A Comprehensive Review](https://mlsanity.com/paper/2310.09411/) (2023)
- [Efficient Encoder-Decoder Transformer Decoding for Decomposable Tasks](https://mlsanity.com/paper/2403.13112/) (2024)
- [SciMRC: Multi-perspective Scientific Machine Reading Comprehension](https://mlsanity.com/paper/2306.14149/) (2023)
- [CCSBench: Evaluating Compositional Controllability in LLMs for Scientific Document Summarization](https://mlsanity.com/paper/2410.12601/) (2024)

## Abstract (arXiv)

Faceted summarization provides briefings of a document from different perspectives. Readers can quickly comprehend the main points of a long document with the help of a structured outline. However, little research has been conducted on this subject, partially due to the lack of large-scale faceted summarization datasets. In this study, we present FacetSum, a faceted summarization benchmark built on Emerald journal articles, covering a diverse range of domains. Different from traditional document-summary pairs, FacetSum provides multiple summaries, each targeted at specific sections of a long document, including the purpose, method, findings, and value. Analyses and empirical results on our dataset reveal the importance of bringing structure into summaries. We believe FacetSum will spur further advances in summarization research and foster the development of NLP systems that can leverage the structured information in both long texts and summaries.