# A Temporally Sensitive Submodularity Framework for Timeline Summarization

arXiv: https://arxiv.org/abs/1810.07949 · HTML: https://mlsanity.com/paper/1810.07949/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2018.

## Built on by (most cited first, 17 of 17)

- [RealTime QA: What's the Answer Right Now?](https://mlsanity.com/paper/2207.13332/) (2022)
- [BiTimeBERT: Extending Pre-Trained Language Representations with Bi-Temporal Information](https://mlsanity.com/paper/2204.13032/) (2022)
- [Combining Hierachical VAEs with LLMs for clinically meaningful timeline summarisation in social media](https://mlsanity.com/paper/2401.16240/) (2024)
- [CrisisLTLSum: A Benchmark for Local Crisis Event Timeline Extraction and Summarization](https://mlsanity.com/paper/2210.14190/) (2022)
- [Just What You Desire: Constrained Timeline Summarization with Self-Reflection for Enhanced Relevance](https://mlsanity.com/paper/2412.17408/) (2024)
- [Follow the Timeline! Generating Abstractive and Extractive Timeline Summary in Chronological Order](https://mlsanity.com/paper/2301.00867/) (2023)
- [Creation and evaluation of timelines for longitudinal user posts](https://mlsanity.com/paper/2303.05891/) (2023)
- [Video Timeline Modeling For News Story Understanding](https://mlsanity.com/paper/2309.13446/) (2023)
- [CNTLS: A Benchmark Dataset for Abstractive or Extractive Chinese Timeline Summarization](https://mlsanity.com/paper/2105.14201/) (2021)
- [Archive TimeLine Summarization (ATLS): Conceptual Framework for Timeline Generation over Historical Document Collections](https://mlsanity.com/paper/2301.13479/) (2023)
- [Synergizing Unsupervised Episode Detection with LLMs for Large-Scale News Events](https://mlsanity.com/paper/2408.04873/) (2024)
- [Placing (Historical) Facts on a Timeline: A Classification cum Coref Resolution Approach](https://mlsanity.com/paper/2206.14089/) (2022)
- [Towards Abstractive Timeline Summarisation using Preference-based Reinforcement Learning](https://mlsanity.com/paper/2211.07596/) (2022)
- [News Signals: An NLP Library for Text and Time Series](https://mlsanity.com/paper/2312.11399/) (2023)
- [TIM: A Large-Scale Dataset and large Timeline Intelligence Model for Open-domain Timeline Summarization](https://mlsanity.com/paper/2506.21616/) (2025)
- [Large Language Model-Powered Query-Driven Event Timeline Summarization in Industrial Search](https://mlsanity.com/paper/2605.27066/) (2026)
- [NTS-CoT: Mitigating Hallucinations in LLM-based News Timeline Summarization with Chain-of-Thought Reasoning](https://mlsanity.com/paper/2606.13171/) (2026)

## Abstract (arXiv)

Timeline summarization (TLS) creates an overview of long-running events via dated daily summaries for the most important dates. TLS differs from standard multi-document summarization (MDS) in the importance of date selection, interdependencies between summaries of different dates and by having very short summaries compared to the number of corpus documents. However, we show that MDS optimization models using submodular functions can be adapted to yield well-performing TLS models by designing objective functions and constraints that model the temporal dimension inherent in TLS. Importantly, these adaptations retain the elegance and advantages of the original MDS models (clear separation of features and inference, performance guarantees and scalability, little need for supervision) that current TLS-specific models lack. An open-source implementation of the framework and all models described in this paper is available online.