Textstar: a Fast and Lightweight Graph-Based Algorithm for Extractive Summarization and Keyphrase Extraction

David Brock, Ali Khan, Tam Doan, Alicia Lin, Yifan Guo, Paul Tarau


Abstract
We introduce Textstar, a graph-based summarization and keyphrase extraction system that builds a document graph using only lemmatization and POS tagging. The document graph aggregates connections between lemma and sentence identifier nodes. Consecutive lemmas in each sentence, as well as consecutive sentences themselves, are connected in rings to form a ring of rings representing the document. We iteratively apply a centrality algorithm of our choice to the document graph and trim the lowest ranked nodes at each step. After the desired number of remaining sentences and lemmas is reached, we extract the sentences as the summary, and the remaining lemmas are aggregated into keyphrases using their context. Our algorithm is efficient enough to one-shot process large document graphs without any training, and empirical evaluation on several benchmarks indicates that our performance is higher than most other graph based algorithms.
Anthology ID:
2022.alta-1.22
Volume:
Proceedings of the The 20th Annual Workshop of the Australasian Language Technology Association
Month:
December
Year:
2022
Address:
Adelaide, Australia
Venue:
ALTA
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Publisher:
Australasian Language Technology Association
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Pages:
161–169
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URL:
https://aclanthology.org/2022.alta-1.22
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Cite (ACL):
David Brock, Ali Khan, Tam Doan, Alicia Lin, Yifan Guo, and Paul Tarau. 2022. Textstar: a Fast and Lightweight Graph-Based Algorithm for Extractive Summarization and Keyphrase Extraction. In Proceedings of the The 20th Annual Workshop of the Australasian Language Technology Association, pages 161–169, Adelaide, Australia. Australasian Language Technology Association.
Cite (Informal):
Textstar: a Fast and Lightweight Graph-Based Algorithm for Extractive Summarization and Keyphrase Extraction (Brock et al., ALTA 2022)
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https://preview.aclanthology.org/emnlp-22-attachments/2022.alta-1.22.pdf