Data-driven Methods of Extracting Text Structure and Information Transfer

Fuente: arXiv
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Main Authors: Honna, Shinichi, Murayama, Taichi, Matsui, Akira
Format: Preprint
Published: 2025
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_version_ 1866909791288295424
author Honna, Shinichi
Murayama, Taichi
Matsui, Akira
author_facet Honna, Shinichi
Murayama, Taichi
Matsui, Akira
contents The Anna Karenina Principle (AKP) holds that success requires satisfying a small set of essential conditions, whereas failure takes diverse forms. We test AKP, its reverse, and two further patterns described as ordered and noisy across novels, online encyclopedias, research papers, and movies. Texts are represented as sequences of functional blocks, and convergence is assessed in transition order and position. Results show that structural principles vary by medium: novels follow reverse AKP in order, Wikipedia combines AKP with ordered patterns, academic papers display reverse AKP in order but remain noisy in position, and movies diverge by genre. Success therefore depends on structural constraints that are specific to each medium, while failure assumes different shapes across domains.
format Preprint
id arxiv_https___arxiv_org_abs_2509_12999
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Data-driven Methods of Extracting Text Structure and Information Transfer
Honna, Shinichi
Murayama, Taichi
Matsui, Akira
Artificial Intelligence
Machine Learning
The Anna Karenina Principle (AKP) holds that success requires satisfying a small set of essential conditions, whereas failure takes diverse forms. We test AKP, its reverse, and two further patterns described as ordered and noisy across novels, online encyclopedias, research papers, and movies. Texts are represented as sequences of functional blocks, and convergence is assessed in transition order and position. Results show that structural principles vary by medium: novels follow reverse AKP in order, Wikipedia combines AKP with ordered patterns, academic papers display reverse AKP in order but remain noisy in position, and movies diverge by genre. Success therefore depends on structural constraints that are specific to each medium, while failure assumes different shapes across domains.
title Data-driven Methods of Extracting Text Structure and Information Transfer
topic Artificial Intelligence
Machine Learning
url https://arxiv.org/abs/2509.12999