| Challenge: | Existing studies try to extract one universal reading order for PDF files, however, some applications, like Retrieval Augmented Generation, require breaking long articles into sections and subsections for better indexing. |
| Approach: | They propose a new task and dataset, PDF-to-Tree, which organizes the text blocks of a PDF into a tree structure. |
| Outcome: | The proposed parser achieves 93.93% accuracy, surpassing baseline methods by 6.72%. |
Similar Papers
PDFdigest: an Adaptable Layout-Aware PDF-to-XML Textual Content Extractor for Scientific Articles (L18-1)
Copied to clipboard
| Challenge: | Existing tools to extract structured textual content from PDFs are essential to enable scientific text mining. |
| Approach: | They propose a PDF-to-XML textual content extraction tool that extracts structured textual contents from scientific articles in PDF format. |
| Outcome: | The proposed tool extracts structured textual content from scientific articles in PDF format while preserving both the textual contents and layout details of the input PDF document. |
On Tree-Based Neural Sentence Modeling (D18-1)
Copied to clipboard
| Challenge: | Existing tree-based sentence modeling approaches adopt syntactic parsing trees as the explicit structure prior. |
| Approach: | They replace parsing trees with trivial trees to study their effectiveness . they found that tree-based sentence modeling gives better results when crucial words are closer to the final representation . |
| Outcome: | The proposed tree-based sentences have shown better results on many downstream tasks. |
Does Structure Matter? Encoding Documents for Machine Reading Comprehension (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing Transformer-based models for machine reading comprehension treat documents as flat sequences. |
| Approach: | They propose a Transformer-based method that reads a document as tree slices and jointly trains and consults the modules at inference time. |
| Outcome: | The proposed method outperforms several baseline approaches on two datasets from varied domains. |
A Scalable Framework for Table of Contents Extraction from Complex ESG Annual Reports (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for document classification focus on local layout, sidelining holistic comprehension of content and organisation. |
| Approach: | They propose a framework for Table of Contents extraction that uses hierarchical structure to extract text from ESG annual reports. |
| Outcome: | The proposed framework outperforms the state-of-the-art with a fraction of running time. |
Discourse Representation Parsing for Sentences and Documents (P19-1)
Copied to clipboard
| Challenge: | Experimental results show that our model outperforms competitive baselines by a wide margin. |
| Approach: | They propose a neural model which parses discourse structures of arbitrary length and granularity. |
| Outcome: | The proposed model outperforms baseline models on sentence- and document-level benchmarks. |
Enriched In-Order Linearization for Faster Sequence-to-Sequence Constituent Parsing (2020.acl-main)
Copied to clipboard
| Challenge: | Sequence-to-sequence constituent parsing requires a linearization to represent trees as sequences. Top-down tree linearizations have achieved the best accuracy to date. |
| Approach: | They propose to use an in-order shift-reduce linearization instead of a top-down tree linearization to represent trees as sequences. |
| Outcome: | The proposed approach achieves the best accuracy to date on the English PTB dataset among fully-supervised single-model sequence-to-sequence constituent parsers. |
TreeRAG: Unleashing the Power of Hierarchical Storage for Enhanced Knowledge Retrieval in Long Documents (2025.findings-acl)
Copied to clipboard
| Challenge: | Traditional RAG frameworks struggle to retrieve all relevant knowledge points . a new approach to retrieve long documents is proposed to improve performance in NLP . |
| Approach: | They propose a tree-based approach to document knowledge retrieval that preserves hierarchical structure . treeRAG is a key technique for enhancing the text generation capabilities of Large Language Models . |
| Outcome: | The proposed approach improves recall quality and precision compared to existing methods and better performance to question-answering tasks. |
DocHieNet: A Large and Diverse Dataset for Document Hierarchy Parsing (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for document hierarchy parsing are limited due to the small scale and inconsistency of datasets. |
| Approach: | They propose a document hierarchy parsing dataset to compensate for the data scarcity problem and propose 'dHP' framework to grasp fine-grained text content and coarse-grounded pattern at layout element level. |
| Outcome: | The proposed framework grasps both fine-grained text content and coarse-grounded pattern at layout element level, enhancing the capacity of pre-trained text-layout models in handling multi-page and multi-level challenges. |
Straight to the Tree: Constituency Parsing with Neural Syntactic Distance (P18-1)
Copied to clipboard
| Challenge: | Compared to traditional shift-reduce parsing schemes, our approach is free from the potentially disastrous compounding error. |
| Approach: | They propose a model that predicts a scalar for each split position in a sentence and then determines the topology of grammar tree based on syntactic distances. |
| Outcome: | The proposed model achieves the state-of-the-art single model F1 score of 92.1 on PTB and 86.4 on CTB dataset, surpassing the previous single model results by a large margin. |
PDF-to-Text Reanalysis for Linguistic Data Mining (L18-1)
Copied to clipboard
| Challenge: | In the 1990s, extracting semistructured text from scientific writing in PDF files was largely a computer vision and OCR problem. |
| Approach: | They propose a system for the reanalysis of PDF-extracted text that performs block detection, respacing, and tabular data analysis for linguistic data mining. |
| Outcome: | The proposed system eliminates the extreme verbosity of XML output while leaving important positional information available for downstream processes. |