# Semantic Parsing of Interpage Relations

arXiv: https://arxiv.org/abs/2205.13530 · HTML: https://mlsanity.com/paper/2205.13530/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 32 references that are in the map.
- First posted on arXiv 26 May 2022.

## Builds on (most cited first, 5 of 5)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Gated Multimodal Units for Information Fusion](https://mlsanity.com/paper/1702.01992/) (2017)
- [Multimodal deep networks for text and image-based document classification](https://mlsanity.com/paper/1907.06370/) (2019)

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

- [One missing piece in Vision and Language: A Survey on Comics Understanding](https://mlsanity.com/paper/2409.09502/) (2024)
- [Large Language Models for Page Stream Segmentation](https://mlsanity.com/paper/2408.11981/) (2024)
- [CoSMo: A Multimodal Transformer for Page Stream Segmentation in Comic Books](https://mlsanity.com/paper/2507.10053/) (2025)
- [Khondo: A Multimodal Benchmark for Document Packet Splitting of Bangla Forms](https://mlsanity.com/paper/2607.21780/) (2026)

## Abstract (arXiv)

Page-level analysis of documents has been a topic of interest in digitization efforts, and multimodal approaches have been applied to both classification and page stream segmentation. In this work, we focus on capturing finer semantic relations between pages of a multi-page document. To this end, we formalize the task as semantic parsing of interpage relations and we propose an end-to-end approach for interpage dependency extraction, inspired by the dependency parsing literature. We further design a multi-task training approach to jointly optimize for page embeddings to be used in segmentation, classification, and parsing of the page dependencies using textual and visual features extracted from the pages. Moreover, we also combine the features from two modalities to obtain multimodal page embeddings. To the best of our knowledge, this is the first study to extract rich semantic interpage relations from multi-page documents. Our experimental results show that the proposed method increased LAS by 41 percentage points for semantic parsing, increased accuracy by 33 percentage points for page stream segmentation, and 45 percentage points for page classification over a naive baseline.