Abstract
In this paper, we present the method proposed by our team for Track 2 of NLPCC 2023 Shared Task 7, which focuses on the extraction of paragraph-level and whole essay topic sentences in middle school student essays. This paper proposes a two-stage topic sentence extraction framework for each paragraph and the whole essay. In the first stage, we extract topic sentences for each paragraph, considering local semantic and contextual aspects. In the second stage, we derive the text topic sentence for the whole essay from the extracted paragraph-level topic sentences. Compared with the one-stage method, the two-stage method which can focus on the local semantic information of paragraphs related to the task has advantages in paragraph and full-text topic sentence extraction. Comparative experiments show that the extraction performance of the fine-tuned two-stage topic sentence extraction framework surpasses the few-shot large language models (GPT-3.5 et al.). The final comprehensive index also achieved the first-place result in this track.
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Dong, Y., Zheng, F., Chen, H., Ding, Y., Zhou, Y., He, H. (2023). Two-Stage Topic Sentence Extraction for Chinese Student Essays. In: Liu, F., Duan, N., Xu, Q., Hong, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2023. Lecture Notes in Computer Science(), vol 14304. Springer, Cham. https://doi.org/10.1007/978-3-031-44699-3_24
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DOI: https://doi.org/10.1007/978-3-031-44699-3_24
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