# TAP-DLND 1.0 : A Corpus for Document Level Novelty Detection

arXiv: https://arxiv.org/abs/1802.06950 · HTML: https://mlsanity.com/paper/1802.06950/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2018.

## Built on by (most cited first, 4 of 4)

- [Automated scholarly paper review: Concepts, technologies, and challenges](https://mlsanity.com/paper/2111.07533/) (2021)
- [NovAScore: A New Automated Metric for Evaluating Document Level Novelty](https://mlsanity.com/paper/2409.09249/) (2024)
- [Multi-Novelty: Improve the Diversity and Novelty of Contents Generated by Large Language Models via inference-time Multi-Views Brainstorming](https://mlsanity.com/paper/2502.12700/) (2025)
- [NoveltyRank: A Retrieval-Augmented Framework for Conceptual Novelty Estimation in AI Research](https://mlsanity.com/paper/2512.14738/) (2025)

## Abstract (arXiv)

Detecting novelty of an entire document is an Artificial Intelligence (AI) frontier problem that has widespread NLP applications, such as extractive document summarization, tracking development of news events, predicting impact of scholarly articles, etc. Important though the problem is, we are unaware of any benchmark document level data that correctly addresses the evaluation of automatic novelty detection techniques in a classification framework. To bridge this gap, we present here a resource for benchmarking the techniques for document level novelty detection. We create the resource via event-specific crawling of news documents across several domains in a periodic manner. We release the annotated corpus with necessary statistics and show its use with a developed system for the problem in concern.