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Constructing and Visualizing Models using Mime-based Machine-learning Framework
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TIPS: A Framework for Text Summarising with Illustrative Pictures.

Justyna Golec1, Tomasz Hachaj1, Grzegorz Sokal1

  • 1Institute of Computer Science, Pedagogical University of Krakow, 2 Podchorazych Ave, 30-084 Krakow, Poland.

Entropy (Basel, Switzerland)
|December 24, 2021
PubMed
Summary

We developed the Text-based Illustrative Picture System (TIPS) algorithm to enhance text summarization with relevant images. TIPS improves image relevance by 3-8% compared to single-embedding methods.

Keywords:
deep learningillustrative imagesimage-text matchingimage-text similaritynatural language processingsemantic multi-modal matchingvoting schema

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Area of Science:

  • Natural Language Processing
  • Computer Vision
  • Information Retrieval

Background:

  • Generating relevant visual aids for text summaries is challenging.
  • Existing methods often rely on single embedding techniques, limiting accuracy.

Purpose of the Study:

  • To propose and evaluate the Text-based Illustrative Picture System (TIPS) algorithm.
  • To improve the selection of illustrative images for text summaries.

Main Methods:

  • TIPS employs a voting process combining results from multiple 'weak' algorithms.
  • Text summarization generates sentence-level input for sentence transformers.
  • CLIP similarity algorithms assess semantic distances between text and image embeddings.
  • A voting mechanism integrates top-scoring image recommendations, down-weighting irrelevant ones.

Main Results:

  • Human judges observed a 5-8% increase in highly relevant image matches.
  • Judges also noted a 3-7% increase in generally relevant images compared to single-embedding approaches.

Conclusions:

  • The TIPS algorithm effectively enhances the relevance of illustrative images for text summaries.
  • The multi-algorithm voting approach in TIPS outperforms single-embedding methods.