Disentangling Instructive Information from Ranked Multiple Candidates for Multi-Document Scientific Summarization

1 code implementation • 16 Apr 2024 • Pancheng Wang, Shasha Li, Dong Li, Kehan Long, Jintao Tang, Ting Wang

Our insights are twofold: Firstly, summary candidates can provide instructive information from both positive and negative perspectives, and secondly, selecting higher-quality candidates from multiple options contributes to producing better summaries.

Recommending Missed Citations Identified by Reviewers: A New Task, Dataset and Baselines

1 code implementation • 4 Mar 2024 • Kehan Long, Shasha Li, Pancheng Wang, Chenlong Bao, Jintao Tang, Ting Wang

To help improve citations of full papers, we first define a novel task of Recommending Missed Citations Identified by Reviewers (RMC) and construct a corresponding expert-labeled dataset called CitationR.

Citation Recommendation Recommendation Systems

Prompting GPT-3.5 for Text-to-SQL with De-semanticization and Skeleton Retrieval

no code implementations • 26 Apr 2023 • Chunxi Guo, Zhiliang Tian, Jintao Tang, Pancheng Wang, Zhihua Wen, Kang Yang, Ting Wang

Text-to-SQL is a task that converts a natural language question into a structured query language (SQL) to retrieve information from a database.

Informativeness Retrieval +2

Multi-Document Scientific Summarization from a Knowledge Graph-Centric View

1 code implementation • COLING 2022 • Pancheng Wang, Shasha Li, Kunyuan Pang, Liangliang He, Dong Li, Jintao Tang, Ting Wang

Multi-Document Scientific Summarization (MDSS) aims to produce coherent and concise summaries for clusters of topic-relevant scientific papers.

Descriptive Knowledge Graphs