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Organic Visualization of Document Evolution

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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Ignacio Perez-Messina, Claudio Gutierrez, Eduardo Graells-Garrido

Human-Computer Interaction · Submitted 17 Dec 2017

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Our results are promising and reveal facets such as general strategies adopted, local edition density and hierarchical structure of the final text.

how it is stated · asserted 0.86 read the record →

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arXiv ID1712.06179 (version 1)
Pith ID5AJZKS6Z, Pith's permanent record of this paper and its review
CitationClaudio Gutierrez, Eduardo Graells-Garrido, Ignacio Perez-Messina. "Organic Visualization of Document Evolution." arXiv:1712.06179 (2017). https://pith.science/paper/5AJZKS6Z
Claim paper

The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

Open the compiled record

Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,153 input tokens.

What the abstract itself states, as probabilities: claims first 0.06 · states limitation 0.34 · human participants 0.94 · controlled comparison 0.03 · randomized assignment 0.02 · states quantitative result 0.04 · compares with prior results 0.06 · mentions code or data availability 0.05.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

Recent availability of data of writing processes at keystroke-granularity has enabled research on the evolution of document writing. A natural step is to develop systems that can actually show this data and make it understandable. Here we propose a data structure that captures a document's fine-grained history and an organic visualization that serves as an interface to it. We evaluate a proof-of-concept implementation of the system through a pilot study with documents written by students at a public university. Our results are promising and reveal facets such as general strategies adopted, local edition density and hierarchical structure of the final text.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Recent availability of data of writing processes at keystroke-granularity has enabled research on the evolution of document writing. background 1.00 0.05 0.02 asserted 1.00
s1 A natural step is to develop systems that can actually show this data and make it understandable. background 0.56 0.06 0.03 asserted 0.88
s2 Here we propose a data structure that captures a document's fine-grained history and an organic visualization that serves as an interface to it. contribution 0.95 0.90 0.11 asserted 0.99
s3 We evaluate a proof-of-concept implementation of the system through a pilot study with documents written by students at a public university. method 0.83 0.96 0.04 asserted 0.98
s4 Our results are promising and reveal facets such as general strategies adopted, local edition density and hierarchical structure of the final text. result 0.87 0.96 0.79 asserted 0.86

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.99.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Recent availability of data of writing processes at keystroke-granularity has enabled research on the evolution of document writing. A natural step is to develop systems that can actually show this data and make it understandable. Here we propose a data structure that captures a document's fine-grained history and an organic visualization that serves as an interface to it. We evaluate a proof-of-concept implementation of the system through a pilot study with documents written by students at a public university. Our results are promising and reveal facets such as general strategies adopted, local edition density and hierarchical structure of the final text.

Human review

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How the literature uses it

What this paper does with the arXiv papers it cites

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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