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Automatically condensing multiple topic-related scientific papers into a succinct and concise summary is referred to as Multi-Document Scientific Summarization (MDSS). Currently, while commonly used abstractive MDSS methods can generate flexible and
Externí odkaz:
http://arxiv.org/abs/2404.10416
Citing comprehensively and appropriately has become a challenging task with the explosive growth of scientific publications. Current citation recommendation systems aim to recommend a list of scientific papers for a given text context or a draft pape
Externí odkaz:
http://arxiv.org/abs/2403.01873
Autor:
Guo, Chunxi, Tian, Zhiliang, Tang, Jintao, Li, Shasha, Wen, Zhihua, Wang, Kaixuan, Wang, Ting
Text-to-SQL aims at generating SQL queries for the given natural language questions and thus helping users to query databases. Prompt learning with large language models (LLMs) has emerged as a recent approach, which designs prompts to lead LLMs to u
Externí odkaz:
http://arxiv.org/abs/2307.05074
Prompt tuning, like CoOp, has recently shown promising vision recognizing and transfer learning ability on various downstream tasks with the emergence of large pre-trained vision-language models like CLIP. However, we identify that existing uni-modal
Externí odkaz:
http://arxiv.org/abs/2306.11400
There is evidence that address matching plays a crucial role in many areas such as express delivery, online shopping and so on. Address has a hierarchical structure, in contrast to unstructured texts, which can contribute valuable information for add
Externí odkaz:
http://arxiv.org/abs/2305.05874
Autor:
Guo, Chunxi, Tian, Zhiliang, Tang, Jintao, Wang, Pancheng, Wen, Zhihua, Yang, Kang, Wang, Ting
Text-to-SQL is a task that converts a natural language question into a structured query language (SQL) to retrieve information from a database. Large language models (LLMs) work well in natural language generation tasks, but they are not specifically
Externí odkaz:
http://arxiv.org/abs/2304.13301
Autor:
Wang, Pancheng, Li, Shasha, Pang, Kunyuan, He, Liangliang, Li, Dong, Tang, Jintao, Wang, Ting
Multi-Document Scientific Summarization (MDSS) aims to produce coherent and concise summaries for clusters of topic-relevant scientific papers. This task requires precise understanding of paper content and accurate modeling of cross-paper relationshi
Externí odkaz:
http://arxiv.org/abs/2209.04319
Publikováno v:
In Information Processing and Management January 2025 62(1)
Publikováno v:
In Information Processing and Management January 2025 62(1)
Publikováno v:
In Expert Systems With Applications 1 October 2024 251