Alexander Martin
Undergraduate Alumnus
Alex is a first-year PhD student in Computer Science at Johns Hopkins University. While in the FACTS.lab, his research focused on information extraction and summarization.
Website
Projects
Papers
- Walden, William, Pavlo Kuchmiichuk, Alexander Martin, Chihsheng Jin, Angela Cao, Claire Sun, Curisia Allen & Aaron White. 2025. Cross-Document Event-Keyed Summarization. In Hao Fei, Kewei Tu, Yuhui Zhang, Xiang Hu, Wenjuan Han, Zixia Jia, Zilong Zheng, et al. (eds.), Proceedings of the 1st Joint Workshop on Large Language Models and Structure Modeling (XLLM 2025), 218–241. Vienna, Austria: Association for Computational Linguistics.
@inproceedings{walden_cross-document_2025,
title = "Cross-Document Event-Keyed Summarization",
author = "Walden, William and
Kuchmiichuk, Pavlo and
Martin, Alexander and
Jin, Chihsheng and
Cao, Angela and
Sun, Claire and
Allen, Curisia and
White, Aaron",
editor = "Fei, Hao and
Tu, Kewei and
Zhang, Yuhui and
Hu, Xiang and
Han, Wenjuan and
Jia, Zixia and
Zheng, Zilong and
Cao, Yixin and
Zhang, Meishan and
Lu, Wei and
Siddharth, N. and
{\O}vrelid, Lilja and
Xue, Nianwen and
Zhang, Yue",
booktitle = "Proceedings of the 1st Joint Workshop on Large Language Models and Structure Modeling (XLLM 2025)",
month = aug,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.xllm-1.19/",
pages = "218--241",
ISBN = "979-8-89176-286-2",
abstract = "Event-keyed summarization (EKS) requires summarizing a specific event described in a document given the document text and an event representation extracted from it. In this work, we extend EKS to the cross-document setting (CDEKS), in which summaries must synthesize information from accounts of the same event as given by multiple sources. We introduce **SEAMuS** (**S**ummaries of **E**vents **A**cross **Mu**ltiple **S**ources), a high-quality dataset for CDEKS based on an expert reannotation of the FAMuS dataset for cross-document argument extraction. We present a suite of baselines on SEAMuS{---}covering both smaller, fine-tuned models, as well as zero- and few-shot prompted LLMs{---}along with detailed ablations and a human evaluation study, showing SEAMuS to be a valuable benchmark for this new task."
}- Vashishtha, Siddharth, Alexander Martin, William Gantt, Benjamin Van Durme & Aaron White. 2024. FAMuS: Frames Across Multiple Sources. In Kevin Duh, Helena Gomez & Steven Bethard (eds.), Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), 8250–8273. Mexico City, Mexico: Association for Computational Linguistics.
@inproceedings{vashishtha_famus_2024,
title = "{FAM}u{S}: Frames Across Multiple Sources",
author = "Vashishtha, Siddharth and
Martin, Alexander and
Gantt, William and
Van Durme, Benjamin and
White, Aaron",
editor = "Duh, Kevin and
Gomez, Helena and
Bethard, Steven",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.naacl-long.457",
doi = "10.18653/v1/2024.naacl-long.457",
pages = "8250--8273",
abstract = "Understanding event descriptions is a central aspect of language processing, but current approaches focus overwhelmingly on single sentences or documents. Aggregating information about an event across documents can offer a much richer understanding. To this end, we present FAMuS, a new corpus of Wikipedia passages that report on some event, paired with underlying, genre-diverse (non-Wikipedia) source articles for the same event. Events and (cross-sentence) arguments in both report and source are annotated against FrameNet, providing broad coverage of different event types. We present results on two key event understanding tasks enabled by FAMuS: source validation{---}determining whether a document is a valid source for a target report event{---}and cross-document argument extraction{---}full-document argument extraction for a target event from both its report and the correct source article.",
}- Gantt, William, Alexander Martin, Pavlo Kuchmiichuk & Aaron White. 2024. Event-Keyed Summarization. In Findings of the Association for Computational Linguistics: EMNLP 2024, 7333–7345. Miami, Florida, USA: Association for Computational Linguistics.
@inproceedings{gantt_event_2024,
title = "Event-Keyed Summarization",
author = "Gantt, William and
Martin, Alexander and
Kuchmiichuk, Pavlo and
White, Aaron",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-emnlp.431",
doi = "10.18653/v1/2024.findings-emnlp.431",
pages = "7333--7345",
}- Barham, Samuel, Orion Weller, Michelle Yuan, Kenton Murray, Mahsa Yarmohammadi, Zhengping Jiang, Siddharth Vashishtha, et al. 2023. MegaWika: Millions of reports and their sources across 50 diverse languages.
@misc{barham_megawika_2023,
title={MegaWika: Millions of reports and their sources across 50 diverse languages},
author={Samuel Barham and Orion Weller and Michelle Yuan and Kenton Murray and Mahsa Yarmohammadi and Zhengping Jiang and Siddharth Vashishtha and Alexander Martin and Anqi Liu and Aaron Steven White and Jordan Boyd-Graber and Benjamin Van Durme},
year={2023},
eprint={2307.07049},
archivePrefix={arXiv},
primaryClass={cs.CL}
}