# On Generating Extended Summaries of Long Documents

arXiv: https://arxiv.org/abs/2012.14136 · HTML: https://mlsanity.com/paper/2012.14136/

## Key facts

- Cited by 10 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 28 Dec 2020.

## Built on by (most cited first, 10 of 10)

- [Recursively Summarizing Books with Human Feedback](https://mlsanity.com/paper/2109.10862/) (2021)
- [A Comprehensive Survey on Process-Oriented Automatic Text Summarization with Exploration of LLM-Based Methods](https://mlsanity.com/paper/2403.02901/) (2024)
- [Incorporating Domain Knowledge for Extractive Summarization of Legal Case Documents](https://mlsanity.com/paper/2106.15876/) (2021)
- [Legal Case Document Summarization: Extractive and Abstractive Methods and their Evaluation](https://mlsanity.com/paper/2210.07544/) (2022)
- [TLDR9+: A Large Scale Resource for Extreme Summarization of Social Media Posts](https://mlsanity.com/paper/2110.01159/) (2021)
- [MILDSum: A Novel Benchmark Dataset for Multilingual Summarization of Indian Legal Case Judgments](https://mlsanity.com/paper/2310.18600/) (2023)
- [TSTR: Too Short to Represent, Summarize with Details! Intro-Guided Extended Summary Generation](https://mlsanity.com/paper/2206.00847/) (2022)
- [Detect, Retrieve, Comprehend: A Flexible Framework for Zero-Shot Document-Level Question Answering](https://mlsanity.com/paper/2210.01959/) (2022)
- [Citance-Contextualized Summarization of Scientific Papers](https://mlsanity.com/paper/2311.02408/) (2023)
- [Stress Testing Factual Consistency Metrics for Long-Document Summarization](https://mlsanity.com/paper/2511.07689/) (2025)

## Abstract (arXiv)

Prior work in document summarization has mainly focused on generating short summaries of a document. While this type of summary helps get a high-level view of a given document, it is desirable in some cases to know more detailed information about its salient points that can't fit in a short summary. This is typically the case for longer documents such as a research paper, legal document, or a book. In this paper, we present a new method for generating extended summaries of long papers. Our method exploits hierarchical structure of the documents and incorporates it into an extractive summarization model through a multi-task learning approach. We then present our results on three long summarization datasets, arXiv-Long, PubMed-Long, and Longsumm. Our method outperforms or matches the performance of strong baselines. Furthermore, we perform a comprehensive analysis over the generated results, shedding insights on future research for long-form summary generation task. Our analysis shows that our multi-tasking approach can adjust extraction probability distribution to the favor of summary-worthy sentences across diverse sections. Our datasets, and codes are publicly available at https://github.com/Georgetown-IR-Lab/ExtendedSumm