Challenge: Existing approaches to producing presentation slides rely on fixed templates or executable code . Existing methods rely only on predefined templates and emit executable codes .
Approach: They propose a hierarchical slides generation workflow DeepSlides that organizes slide design tasks without any predefined template or style.
Outcome: The proposed framework outperforms baseline methods on evaluated metrics and achieves superior performance in human preference evaluations.

Similar Papers

SlideCoder: Layout-aware RAG-enhanced Hierarchical Slide Generation from Design (2025.emnlp-main)

Copied to clipboard

Challenge: Existing natural language-based LLM generation methods struggle to capture visual and structural nuances of slide designs.
Approach: They propose a layout-aware framework for generating editable slides from reference images . they propose python code that translates NL instructions into Python code to construct each slide .
Outcome: The proposed framework outperforms state-of-the-art models by up to 40.5 points . it also outperformed open-source models with improved reverse-engineered data.
PreGenie: An Agentic Framework for High-quality Visual Presentation Generation (2025.findings-emnlp)

Copied to clipboard

Challenge: Visual presentations are vital for effective communication, but they are limited by their complexity and lack of visual understanding.
Approach: a new framework is proposed to generate high-quality visual presentations using multimodal large language models.
Outcome: The proposed framework outperforms existing models in multimodal understanding and content consistency.
DeepPresenter: Environment-Grounded Reflection for Agentic Presentation Generation (2026.findings-acl)

Copied to clipboard

Challenge: Existing presentation agents rely on predefined workflows and fixed templates to generate presentations.
Approach: They propose an agentic framework that adapts to diverse user intents and iterative refinement based on observation.
Outcome: The proposed framework can be used to generate presentations with environmental observations.
PosterForest: Hierarchical Multi-Agent Collaboration for Scientific Poster Generation (2026.acl-long)

Copied to clipboard

Challenge: Existing methods for scientific poster generation lack hierarchical document understanding and coherent content-layout planning.
Approach: They propose a training-free framework for scientific poster generation that captures document hierarchy and semantics across multiple levels.
Outcome: The proposed framework outperforms existing methods in both automatic and human evaluations without additional training or domain-specific supervision.
PPTAgent: Generating and Evaluating Presentations Beyond Text-to-Slides (2025.emnlp-main)

Copied to clipboard

Challenge: Existing methods for generating presentations from documents focus on improving and evaluating content quality in isolation, overlooking visual appeal and structural coherence.
Approach: They propose an edit-based presentation generation system that analyzes and iterates on slides to create new slides.
Outcome: The proposed presentation generation tool outperforms existing methods in three dimensions . it analyzes slides, iterates and generates edit actions based on selected slides .
Presentations by the Humans and For the Humans: Harnessing LLMs for Generating Persona-Aware Slides from Documents (2024.eacl-long)

Copied to clipboard

Challenge: Existing efforts to automate document-to-slide generation have failed to adapt to the persona of target audience or duration of presentation.
Approach: They propose a concept of end-user specification-aware document to slides conversion that incorporates end- user specifications into the conversion process.
Outcome: The proposed model can create persona-aware presentations tailored to the persona of target audience and cognitive abilities of target audiences.
Automatic Slide Updating with User-Defined Dynamic Templates and Natural Language Instructions (2026.findings-acl)

Copied to clipboard

Challenge: Existing automation methods follow fixed template filling and cannot support dynamic updates for diverse, user-authored decks.
Approach: They propose a framework that combines multimodal slide parsing, natural language instruction grounding, and tool-augmented reasoning for tables, charts, and textual conclusions.
Outcome: The proposed framework updates content while preserving layout and style while maintaining a strong reference baseline on DynaSlide.
Sentence-Level Content Planning and Style Specification for Neural Text Generation (D19-1)

Copied to clipboard

Challenge: Recent advances in text generation systems often produce incoherent and unfaithful outputs . a novel automated text generation system takes into account content selection, text planning, and surface realization.
Approach: They propose an end-to-end trained two-step text generation model that considers sentence-level content planners and language styles.
Outcome: The proposed model outperforms competing models in three domains with diverse topics and varying language styles.
Tell2Design: A Dataset for Language-Guided Floor Plan Generation (2023.acl-long)

Copied to clipboard

Challenge: Recent studies have demonstrated impressive results in generating high-fidelity artistic images.
Approach: They propose a Sequence-to-Sequence model that can serve as a strong baseline for future research.
Outcome: The proposed model can be used as a baseline for future research and human evaluations are conducted on the generated samples and provided an analysis of human performance.
D2S: Document-to-Slide Generation Via Query-Based Text Summarization (2021.naacl-main)

Copied to clipboard

Challenge: Existing research efforts to automate the document-to-slide generation process face a critical challenge: no publicly available dataset for training and benchmarking.
Approach: They propose a dataset SciDuet that gathers papers and their corresponding slides from recent years’ NLP and ML conferences.
Outcome: The proposed system outperforms state-of-the-art summarization baselines on both automated ROUGE metrics and qualitative human evaluation.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations