ORACLE: Time-Dependent Recursive Summary Graphs for Foresight on News Data Using LLMs
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Abstract (arXiv)
ORACLE turns daily news into week-over-week, decision-ready insights for one of the Finnish University of Applied Sciences. The platform crawls and versions news, applies University-specific relevance filtering, embeds content, classifies items into PESTEL dimensions and builds a concise Time-Dependent Recursive Summary Graph (TRSG): two clustering layers summarized by an LLM and recomputed weekly. A lightweight change detector highlights what is new, removed or changed, then groups differences into themes for PESTEL-aware analysis. We detail the pipeline, discuss concrete design choices that make the system stable in production and present a curriculum-intelligence use case with an evaluation plan.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 19 references that are in the map. First posted on arXiv 17 Dec 2025.
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7 of 19 references.
References · 7
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
- 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
- 2017Billion-scale similarity search with GPUscited by 2,497
- 2021SimCSE: Simple Contrastive Learning of Sentence Embeddingscited by 2,173
- 2019PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarizationcited by 1,102
- 2022BERTopic: Neural topic modeling with a class-based TF-IDF procedurecited by 801
- 2018From Louvain to Leiden: guaranteeing well-connected communitiescited by 385
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