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
Papers
- Walden, William, Pavlo Kuchmiichuk, Alexander Martin, Chihsheng Jin, Angela Cao, Claire Sun, Curisia Allen & Aaron Steven White. 2024. Cross-Document Event-Keyed Summarization.
@misc{walden_cross-document_2024,
title={Cross-Document Event-Keyed Summarization},
author={William Walden and Pavlo Kuchmiichuk and Alexander Martin and Chihsheng Jin and Angela Cao and Claire Sun and Curisia Allen and Aaron Steven White},
year={2024},
eprint={2410.14795},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2410.14795},
}- 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 Steven White. 2024. Event-Keyed Summarization.
- 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}
}