Gabriele Pergola
@pergolagb.bsky.social
Assistant Professor in Natural Language Processing (NLP) at the University of Warwick, UK.
Personal Webpage: https://warwick.ac.uk/fac/sci/dcs/people/u1898418/
Personal Webpage: https://warwick.ac.uk/fac/sci/dcs/people/u1898418/
🚀 Takeaway: SciGisPy is a novel library for domain-specific text evaluation, enabling automatic simplification (#ATS) for technical fields. Dive into the full details here: arxiv.org/abs/2410.09632. 🙌
#EMNLP #ACL #NLP #TextSimplification
#EMNLP #ACL #NLP #TextSimplification
SciGisPy: a Novel Metric for Biomedical Text Simplification via Gist Inference Score
Biomedical literature is often written in highly specialized language, posing significant comprehension challenges for non-experts. Automatic text simplification (ATS) offers a solution by making such...
arxiv.org
November 28, 2024 at 6:35 PM
🚀 Takeaway: SciGisPy is a novel library for domain-specific text evaluation, enabling automatic simplification (#ATS) for technical fields. Dive into the full details here: arxiv.org/abs/2410.09632. 🙌
#EMNLP #ACL #NLP #TextSimplification
#EMNLP #ACL #NLP #TextSimplification
📊 Impactful Results:
- On the Cochrane biomedical dataset, SciGisPy correctly identifies simplified texts in 84% of cases, compared to 44.8% for SARI.
- Ablation studies confirm the contributions of semantic chunking, cohesion, and sentence-level measures.
- On the Cochrane biomedical dataset, SciGisPy correctly identifies simplified texts in 84% of cases, compared to 44.8% for SARI.
- Ablation studies confirm the contributions of semantic chunking, cohesion, and sentence-level measures.
November 28, 2024 at 6:35 PM
📊 Impactful Results:
- On the Cochrane biomedical dataset, SciGisPy correctly identifies simplified texts in 84% of cases, compared to 44.8% for SARI.
- Ablation studies confirm the contributions of semantic chunking, cohesion, and sentence-level measures.
- On the Cochrane biomedical dataset, SciGisPy correctly identifies simplified texts in 84% of cases, compared to 44.8% for SARI.
- Ablation studies confirm the contributions of semantic chunking, cohesion, and sentence-level measures.
⚙️ Refined Metric Design: SciGisPy improves on GIS (Gist Inference Score) by:
- Removing indices unsuitable for biomedical contexts (e.g., word imageability).
- Adding metrics for sentence length & cohesion.
- Revising WordNet-based hypernym paths with domain-specific IC measures.
- Removing indices unsuitable for biomedical contexts (e.g., word imageability).
- Adding metrics for sentence length & cohesion.
- Revising WordNet-based hypernym paths with domain-specific IC measures.
November 28, 2024 at 6:35 PM
⚙️ Refined Metric Design: SciGisPy improves on GIS (Gist Inference Score) by:
- Removing indices unsuitable for biomedical contexts (e.g., word imageability).
- Adding metrics for sentence length & cohesion.
- Revising WordNet-based hypernym paths with domain-specific IC measures.
- Removing indices unsuitable for biomedical contexts (e.g., word imageability).
- Adding metrics for sentence length & cohesion.
- Revising WordNet-based hypernym paths with domain-specific IC measures.
🌟 What's new:
- Introduces semantic chunking to measure text coherence.
- Incorporates information content theory for better word specificity.
- Uses #biomedical embeddings (e.g., #BioWordVec, #BioSimCSE) to capture complex concepts.
- Introduces semantic chunking to measure text coherence.
- Incorporates information content theory for better word specificity.
- Uses #biomedical embeddings (e.g., #BioWordVec, #BioSimCSE) to capture complex concepts.
November 28, 2024 at 6:35 PM
🌟 What's new:
- Introduces semantic chunking to measure text coherence.
- Incorporates information content theory for better word specificity.
- Uses #biomedical embeddings (e.g., #BioWordVec, #BioSimCSE) to capture complex concepts.
- Introduces semantic chunking to measure text coherence.
- Incorporates information content theory for better word specificity.
- Uses #biomedical embeddings (e.g., #BioWordVec, #BioSimCSE) to capture complex concepts.
🔍 What’s SciGisPy?: SciGisPy evaluates #gist inference - how well #simplified texts convey their essential meaning or core ideas.
Inspired by #Fuzzy-Trace Theory, it bridges linguistic simplicity with comprehension of critical content, especially for domain-specific texts.
Inspired by #Fuzzy-Trace Theory, it bridges linguistic simplicity with comprehension of critical content, especially for domain-specific texts.
November 28, 2024 at 6:35 PM
🔍 What’s SciGisPy?: SciGisPy evaluates #gist inference - how well #simplified texts convey their essential meaning or core ideas.
Inspired by #Fuzzy-Trace Theory, it bridges linguistic simplicity with comprehension of critical content, especially for domain-specific texts.
Inspired by #Fuzzy-Trace Theory, it bridges linguistic simplicity with comprehension of critical content, especially for domain-specific texts.
📝 Challenges & Solutions:
1️⃣ Balancing Accuracy & Simplicity: Agents are tuned to avoid oversimplification that loses key medical details
2️⃣ Time Complexity: Parallel processing and efficient feedback mechanisms minimize delays.
1️⃣ Balancing Accuracy & Simplicity: Agents are tuned to avoid oversimplification that loses key medical details
2️⃣ Time Complexity: Parallel processing and efficient feedback mechanisms minimize delays.
November 27, 2024 at 5:36 PM
📝 Challenges & Solutions:
1️⃣ Balancing Accuracy & Simplicity: Agents are tuned to avoid oversimplification that loses key medical details
2️⃣ Time Complexity: Parallel processing and efficient feedback mechanisms minimize delays.
1️⃣ Balancing Accuracy & Simplicity: Agents are tuned to avoid oversimplification that loses key medical details
2️⃣ Time Complexity: Parallel processing and efficient feedback mechanisms minimize delays.
🔄 Interaction Loop:
The agents collaborate through an iterative refinement loop:
1️⃣ Propose: Agents generate initial simplifications independently.
2️⃣ Evaluate: Feedback is collected via scoring mechanisms.
3️⃣ Refine: Agents adjust simplifications based on collective input.
The agents collaborate through an iterative refinement loop:
1️⃣ Propose: Agents generate initial simplifications independently.
2️⃣ Evaluate: Feedback is collected via scoring mechanisms.
3️⃣ Refine: Agents adjust simplifications based on collective input.
November 27, 2024 at 5:35 PM
🔄 Interaction Loop:
The agents collaborate through an iterative refinement loop:
1️⃣ Propose: Agents generate initial simplifications independently.
2️⃣ Evaluate: Feedback is collected via scoring mechanisms.
3️⃣ Refine: Agents adjust simplifications based on collective input.
The agents collaborate through an iterative refinement loop:
1️⃣ Propose: Agents generate initial simplifications independently.
2️⃣ Evaluate: Feedback is collected via scoring mechanisms.
3️⃣ Refine: Agents adjust simplifications based on collective input.
🤖 Agent Roles in our framework:
1️⃣ Medical Terminology Simplifier: Simplifies technical jargon while preserving meaning.
2️⃣ Sentence Rewriter: Breaks down complex sentence structures.
3️⃣ Coherence Validator: Ensures text flow remains logical post-simplification.
1️⃣ Medical Terminology Simplifier: Simplifies technical jargon while preserving meaning.
2️⃣ Sentence Rewriter: Breaks down complex sentence structures.
3️⃣ Coherence Validator: Ensures text flow remains logical post-simplification.
November 27, 2024 at 5:35 PM
🤖 Agent Roles in our framework:
1️⃣ Medical Terminology Simplifier: Simplifies technical jargon while preserving meaning.
2️⃣ Sentence Rewriter: Breaks down complex sentence structures.
3️⃣ Coherence Validator: Ensures text flow remains logical post-simplification.
1️⃣ Medical Terminology Simplifier: Simplifies technical jargon while preserving meaning.
2️⃣ Sentence Rewriter: Breaks down complex sentence structures.
3️⃣ Coherence Validator: Ensures text flow remains logical post-simplification.
🔬 The “Society of Medical Simplifiers” builds on the idea that multiple specialized agents can collaborate to simplify medical texts. Each agent has a unique role, ensuring a balance between clarity and technical accuracy.
Here’s how it works: 👇
Here’s how it works: 👇
November 27, 2024 at 5:34 PM
🔬 The “Society of Medical Simplifiers” builds on the idea that multiple specialized agents can collaborate to simplify medical texts. Each agent has a unique role, ensuring a balance between clarity and technical accuracy.
Here’s how it works: 👇
Here’s how it works: 👇