Digging Up Citations: FOSSIL, a Dataset and Workflow for Reference Extraction in Law and the Humanities

• URL: http://arxiv.org/abs/2606.01109v1
• Date: Sun, 31 May 2026 08:59:49 GMT
• Title: Digging Up Citations: FOSSIL, a Dataset and Workflow for Reference Extraction in Law and the Humanities
• Abstract summary: We present an open-access, multilingual dataset of 96 annotated articles containing over 7,600 footnote-embedded references.<n>In end-to-end evaluation, the specialized pipeline nearly doubles extraction over default Grobid.<n>This extended abstract presents work in progress; citations segmentation and parsing, and cross-reference resolution are ongoing.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Citation extraction tools are designed for the structured end-of-document bibliographies of the natural sciences, but law and humanities scholarship cites references primarily in footnotes, where bibliographic data is interleaved with commentary and cross-references and varies widely across languages and styles. To address the scarcity of suitable gold-standard resources, we present FOSSIL (Footnote-based Open-access SSH Scientific Instance Labels), an openly licensed multilingual dataset of 96 annotated scholarly articles containing over 7,600 footnote-embedded references, together with PDF-TEI Editor (a collaborative web annotation tool), a documented seven-annotator workflow, and a Grobid specialization for footnote-based citations. In end-to-end evaluation, the specialized pipeline nearly doubles extraction quality over default Grobid (micro-F1 from 0.36 to 0.72), driven largely by improved recall, while showing that substantial headroom remains for cross-references and mixed-content footnotes. This extended abstract presents work in progress; annotations of citations segmentation and parsing, and cross-reference resolution are ongoing.

Related papers

• DocAtlas: Multilingual Document Understanding Across 80+ Languages [58.715440331861295]
  We introduce DocAtlas, a framework that constructs high-fidelity OCR datasets and benchmarks covering 82 languages.<n>Our dual pipelines, differential rendering of native DOCX documents and synthetic-based generation for right-to-left scripts produce precise structural annotations.
  arXiv  Detail & Related papers  (2026-05-12T18:09:38Z)
• Benchmarking Large Language Models on Reference Extraction and Parsing in the Social Sciences and Humanities [11.04552942214836]
  Most established evaluations focus on clean, English, end-of-document, and therefore underrepresent the Social Sciences and Humanities.<n>We present a unified benchmark that targets these SSH-realistic conditions across three complementary datasets.<n>We evaluate three tasks of increasing difficulty -- reference extraction, reference parsing, and end-to-end document parsing.
  arXiv  Detail & Related papers  (2026-03-13T23:25:03Z)
• Semantically Orthogonal Framework for Citation Classification: Disentangling Intent and Content [0.0]
  SOFT is a Semantically Orthogonal Framework with Two dimensions that explicitly separates citation intent from cited content type.<n>We re-annotate the ACL-ARC dataset using SOFT and release a cross-disciplinary test set sampled from ACT2.<n>Results confirm SOFT's value as a clear, reusable annotation standard, improving clarity, consistency, and generalizability for digital libraries and scholarly communication infrastructures.
  arXiv  Detail & Related papers  (2026-01-08T16:48:36Z)
• Cleaning English Abstracts of Scientific Publications [0.15293427903448018]
  We introduce an open-source, easy-to-integrate language model designed to clean English-language scientific abstracts.<n>We demonstrate that our model is both conservative and precise, alters similarity rankings of cleaned abstracts and improves information content of standard-length embeddings.
  arXiv  Detail & Related papers  (2025-12-30T20:45:50Z)
• Cite Pretrain: Retrieval-Free Knowledge Attribution for Large Language Models [44.31597857713689]
  We introduce Active Indexing for the first stage, which creates generalizable, source-anchored bindings.<n>Experiments with Qwen-2.5-7B&3B show that Active Indexing consistently outperforms a Passive Indexing baseline.<n> internal citations complement external ones by making the model more robust to retrieval noise.
  arXiv  Detail & Related papers  (2025-06-21T04:48:05Z)
• Verifiable Generation with Subsentence-Level Fine-Grained Citations [13.931548733211436]
  Verifiable generation requires large language models to cite source documents supporting their outputs. Previous work mainly targets the generation of sentence-level citations, lacking specificity about which parts of a sentence are backed by the cited sources. This work studies verifiable generation with subsentence-level fine-grained citations for more precise location of generated content supported by the cited sources.
  arXiv  Detail & Related papers  (2024-06-10T09:32:37Z)
• On Context Utilization in Summarization with Large Language Models [83.84459732796302]
  Large language models (LLMs) excel in abstractive summarization tasks, delivering fluent and pertinent summaries. Recent advancements have extended their capabilities to handle long-input contexts, exceeding 100k tokens. We conduct the first comprehensive study on context utilization and position bias in summarization.
  arXiv  Detail & Related papers  (2023-10-16T16:45:12Z)
• CiteBench: A benchmark for Scientific Citation Text Generation [69.37571393032026]
  CiteBench is a benchmark for citation text generation. We make the code for CiteBench publicly available at https://github.com/UKPLab/citebench.
  arXiv  Detail & Related papers  (2022-12-19T16:10:56Z)
• Assessing the quality of sources in Wikidata across languages: a hybrid approach [64.05097584373979]
  We run a series of microtasks experiments to evaluate a large corpus of references, sampled from Wikidata triples with labels in several languages. We use a consolidated, curated version of the crowdsourced assessments to train several machine learning models to scale up the analysis to the whole of Wikidata. The findings help us ascertain the quality of references in Wikidata, and identify common challenges in defining and capturing the quality of user-generated multilingual structured data on the web.
  arXiv  Detail & Related papers  (2021-09-20T10:06:46Z)
• Enhancing Scientific Papers Summarization with Citation Graph [78.65955304229863]
  We redefine the task of scientific papers summarization by utilizing their citation graph. We construct a novel scientific papers summarization dataset Semantic Scholar Network (SSN) which contains 141K research papers in different domains. Our model can achieve competitive performance when compared with the pretrained models.
  arXiv  Detail & Related papers  (2021-04-07T11:13:35Z)
• Long Document Summarization in a Low Resource Setting using Pretrained Language Models [28.042826329840437]
  We study a challenging low-resource setting of summarizing long legal briefs with an average source document length of 4268 words. We use a modern pretrained abstractive summarizer BART, which only achieves 17.9 ROUGE-L as it struggles with long sentences. On feeding the compressed documents to BART, we observe a 6.0 ROUGE-L improvement.
  arXiv  Detail & Related papers  (2021-03-01T04:43:55Z)
• SPECTER: Document-level Representation Learning using Citation-informed Transformers [51.048515757909215]
  SPECTER generates document-level embedding of scientific documents based on pretraining a Transformer language model. We introduce SciDocs, a new evaluation benchmark consisting of seven document-level tasks ranging from citation prediction to document classification and recommendation.
  arXiv  Detail & Related papers  (2020-04-15T16:05:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.