Challenge: Existing approaches to generate presentations from document to slide are difficult to implement and cause hallucination.
Approach: They propose a graph-based solution that uses a combination of graph neural network and LLM to generate a presentation with attribution of content for each slide.
Outcome: The proposed approach is more efficient than using LLMs for generating a presentation from the text of a document.

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Connecting the Dots: What Graph-Based Text Representations Work Best for Text Classification using Graph Neural Networks? (2023.findings-emnlp)

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Challenge: Graph Neural Networks have been used for text classification, but only in domains with limited data characteristics.
Approach: They compare graph representation methods for text classification using different architectures and setups.
Outcome: The proposed graph representation methods outperform other models in document comprehension tasks.
Searching for Effective Neural Extractive Summarization: What Works and What’s Next (P19-1)

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Challenge: Recent years have seen success in the use of deep neural networks on text summarization, but there is no clear understanding of why they perform so well or how they might be improved.
Approach: They propose to use different types of model architectures to improve extractive summarization systems.
Outcome: The proposed framework achieves state-of-the-art on CNN/DailyMail by a large margin based on observations and analysis.
Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks (2020.acl-main)

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Challenge: Existing graph-based methods for text classification cannot capture contextual word relationships within each document nor can they produce inductive learning of new words.
Approach: They propose to use Graph Neural Networks to learn the local word representations and then aggregate the word nodes as the document embeddings.
Outcome: The proposed method outperforms state-of-the-art methods on four benchmark datasets.
Presentations by the Humans and For the Humans: Harnessing LLMs for Generating Persona-Aware Slides from Documents (2024.eacl-long)

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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.
A Survey of Automatic Text Summarization Using Graph Neural Networks (2022.coling-1)

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Challenge: Abstractive ATS involves generating factually correct and fluent sentences.
Approach: They provide an overview of the use of graph neural networks (GNNs) for automatic text summarization.
Outcome: The proposed model is based on a set of graph neural networks (GNNs) that are used to generate a concise, correct and fluent summary of a given text.
Can LLMs Convert Graphs to Text-Attributed Graphs? (2025.naacl-long)

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Challenge: Existing approaches to model graph-structured data are limited by the availability of text-attributed graph data.
Approach: They propose a method to convert existing graphs into text-attributed graphs using large language models.
Outcome: The proposed method outperforms existing approaches that manually design node features on text-free graphs.
Modeling Global and Local Node Contexts for Text Generation from Knowledge Graphs (2020.tacl-1)

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Challenge: Recent graph-to-text models generate text from graph data using global or local aggregation . global node encoding allows explicit communication between two distant nodes, but fails to capture long-range relationships.
Approach: They propose to combine global and local aggregation to learn node representations . they propose to use global and locally encoding to learn contextualized node embeddings based on graph data .
Outcome: The proposed models outperform state-of-the-art models on two graph-to-text datasets by 18.01 and 63.69 points.
A Graph-to-Sequence Model for AMR-to-Text Generation (P18-1)

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Challenge: Abstract Meaning Representation (AMR) is a semantic formalism that encodes the meaning of a sentence as a rooted, directed graph.
Approach: They propose a neural graph-to-sequence model that leverages LSTM to encode a linearized AMR structure.
Outcome: The proposed model outperforms existing methods on a benchmark.
Text Graph Transformer for Document Classification (2020.emnlp-main)

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Challenge: Existing methods for text classification are not scalable to large corpus and ignore heterogeneity of text graph.
Approach: They propose a Transformer-based heterogeneous graph neural network that captures structure and heterogenity from the text graph.
Outcome: The proposed model outperforms state-of-the-art methods on large-sized corpus datasets and significantly reduces computing and memory costs.
D2S: Document-to-Slide Generation Via Query-Based Text Summarization (2021.naacl-main)

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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.

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