@inproceedings{lin-etal-2025-discosg,
title = "{D}isco{SG}: Towards Discourse-Level Text Scene Graph Parsing through Iterative Graph Refinement",
author = "Lin, Shaoqing and
Teng, Chong and
Li, Fei and
Ji, Donghong and
Qu, Lizhen and
Li, Zhuang",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.398/",
doi = "10.18653/v1/2025.emnlp-main.398",
pages = "7837--7862",
ISBN = "979-8-89176-332-6",
abstract = "Vision-Language Models (VLMs) generate discourse-level, multi-sentence visual descriptions, challenging text scene graph parsers built for single-sentence caption-to-graph mapping. Current approaches typically merge sentence-level parsing outputs for discourse input, often missing phenomena like cross-sentence coreference, resulting in fragmented graphs and degraded downstream VLM task performance. We introduce a new task, Discourse-level text Scene Graph parsing (DiscoSG), and release DiscoSG-DS, a dataset of 400 expert-annotated and 8,430 synthesised multi-sentence caption-graph pairs. Each caption averages 9 sentences, and each graph contains at least 3$\times$ more triples than those in existing datasets. Fine-tuning GPT-4o on DiscoSG-DS yields over 40{\%} higher SPICE than the strongest sentence-merging baseline. However, its high inference cost and licensing restrict open-source use, and smaller fine-tuned open-source models (e.g., Flan-T5) perform poorly on dense graph generation. To bridge this gap, we propose DiscoSG-Refiner, which drafts a base graph using a seed parser and iteratively refines it with a second model, improving robustness for complex graph generation. Using two small fine-tuned Flan-T5-Base models, DiscoSG-Refiner improves SPICE by {\textasciitilde}30{\%} over the baseline while achieving $86{\times}$ faster inference than GPT-4o. It also delivers consistent gains on downstream VLM tasks, including discourse-level caption evaluation and hallucination detection, outperforming alternative parsers. Code and data are available at https://github.com/ShaoqLin/DiscoSG ."
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<abstract>Vision-Language Models (VLMs) generate discourse-level, multi-sentence visual descriptions, challenging text scene graph parsers built for single-sentence caption-to-graph mapping. Current approaches typically merge sentence-level parsing outputs for discourse input, often missing phenomena like cross-sentence coreference, resulting in fragmented graphs and degraded downstream VLM task performance. We introduce a new task, Discourse-level text Scene Graph parsing (DiscoSG), and release DiscoSG-DS, a dataset of 400 expert-annotated and 8,430 synthesised multi-sentence caption-graph pairs. Each caption averages 9 sentences, and each graph contains at least 3\times more triples than those in existing datasets. Fine-tuning GPT-4o on DiscoSG-DS yields over 40% higher SPICE than the strongest sentence-merging baseline. However, its high inference cost and licensing restrict open-source use, and smaller fine-tuned open-source models (e.g., Flan-T5) perform poorly on dense graph generation. To bridge this gap, we propose DiscoSG-Refiner, which drafts a base graph using a seed parser and iteratively refines it with a second model, improving robustness for complex graph generation. Using two small fine-tuned Flan-T5-Base models, DiscoSG-Refiner improves SPICE by ~30% over the baseline while achieving 86\times faster inference than GPT-4o. It also delivers consistent gains on downstream VLM tasks, including discourse-level caption evaluation and hallucination detection, outperforming alternative parsers. Code and data are available at https://github.com/ShaoqLin/DiscoSG .</abstract>
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%0 Conference Proceedings
%T DiscoSG: Towards Discourse-Level Text Scene Graph Parsing through Iterative Graph Refinement
%A Lin, Shaoqing
%A Teng, Chong
%A Li, Fei
%A Ji, Donghong
%A Qu, Lizhen
%A Li, Zhuang
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F lin-etal-2025-discosg
%X Vision-Language Models (VLMs) generate discourse-level, multi-sentence visual descriptions, challenging text scene graph parsers built for single-sentence caption-to-graph mapping. Current approaches typically merge sentence-level parsing outputs for discourse input, often missing phenomena like cross-sentence coreference, resulting in fragmented graphs and degraded downstream VLM task performance. We introduce a new task, Discourse-level text Scene Graph parsing (DiscoSG), and release DiscoSG-DS, a dataset of 400 expert-annotated and 8,430 synthesised multi-sentence caption-graph pairs. Each caption averages 9 sentences, and each graph contains at least 3\times more triples than those in existing datasets. Fine-tuning GPT-4o on DiscoSG-DS yields over 40% higher SPICE than the strongest sentence-merging baseline. However, its high inference cost and licensing restrict open-source use, and smaller fine-tuned open-source models (e.g., Flan-T5) perform poorly on dense graph generation. To bridge this gap, we propose DiscoSG-Refiner, which drafts a base graph using a seed parser and iteratively refines it with a second model, improving robustness for complex graph generation. Using two small fine-tuned Flan-T5-Base models, DiscoSG-Refiner improves SPICE by ~30% over the baseline while achieving 86\times faster inference than GPT-4o. It also delivers consistent gains on downstream VLM tasks, including discourse-level caption evaluation and hallucination detection, outperforming alternative parsers. Code and data are available at https://github.com/ShaoqLin/DiscoSG .
%R 10.18653/v1/2025.emnlp-main.398
%U https://aclanthology.org/2025.emnlp-main.398/
%U https://doi.org/10.18653/v1/2025.emnlp-main.398
%P 7837-7862
Markdown (Informal)
[DiscoSG: Towards Discourse-Level Text Scene Graph Parsing through Iterative Graph Refinement](https://aclanthology.org/2025.emnlp-main.398/) (Lin et al., EMNLP 2025)
ACL