# Hierarchical Book Organization for Learning-Resource Discovery using Dual-Path Graph Convolutions

arXiv: https://arxiv.org/abs/2512.21076 · HTML: https://mlsanity.com/paper/2512.21076/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 52 references that are in the map.
- First posted on arXiv 24 Dec 2025.

## Builds on (most cited first, 22 of 22)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
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- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Gemma: Open Models Based on Gemini Research and Technology](https://mlsanity.com/paper/2403.08295/) (2024)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference](https://mlsanity.com/paper/2412.13663/) (2024)
- [Graph Convolutional Networks for Text Classification](https://mlsanity.com/paper/1809.05679/) (2018)
- [BertGCN: Transductive Text Classification by Combining GCN and BERT](https://mlsanity.com/paper/2105.05727/) (2021)
- [HiTIN: Hierarchy-aware Tree Isomorphism Network for Hierarchical Text Classification](https://mlsanity.com/paper/2305.15182/) (2023)
- [HILL: Hierarchy-aware Information Lossless Contrastive Learning for Hierarchical Text Classification](https://mlsanity.com/paper/2403.17307/) (2024)
- [Correlation-Aware Graph Convolutional Networks for Multi-Label Node Classification](https://mlsanity.com/paper/2411.17350/) (2024)
- [Deep multi-modal networks for book genre classification based on its cover](https://mlsanity.com/paper/2011.07658/) (2020)
- [What Text Design Characterizes Book Genres?](https://mlsanity.com/paper/2402.16356/) (2024)

## Abstract (arXiv)

The growing availability of books and textual materials in digital learning environments necessitates reliable semantic organization to support scalable resource management and discovery. However, existing book classification approaches typically formulate genre prediction as a flat classification problem, overlooking both the hierarchical organization of literary categories and the semantic discrepancy between authoritative book descriptions and subjective crowd-sourced reviews. We propose titleabbr, a hierarchical book classification framework for structured learning-resource organization that reformulates genre prediction as coarse-to-fine semantic reasoning over heterogeneous textual evidence. HiGeMine first performs blurb-guided semantic refinement to preserve semantically consistent reviews while suppressing noisy and genre-irrelevant interpretations. It then performs semantic-role-separated hierarchical graph reasoning through independent propagation branches for blurbs and reviews, enabling separate modeling of narrative and interpretive semantics during hierarchical inference. A coarse-grained level-1 classifier first distinguishes fiction from non-fiction, followed by domain-specialized level-2 multi-label classifiers for fine-grained genre prediction. HiGeMine captures relationships among fine-grained resource categories through a structured label co-occurrence graph and genre-conditioned semantic representations. To facilitate systematic evaluation, we curate a new hierarchical multi-label Goodreads benchmark containing paired blurbs and reviews. Experiments against hierarchical classifiers, graph-based approaches, pretrained LMs, and LLMs demonstrate the effectiveness of HiGeMine for reliable hierarchical book classification, thereby providing a scalable foundation for organizing and discovering textual resources in digital learning environments.