Summarizing Utterances from Japanese Assembly Minutes using Political Sentence-BERT-based Method for QA Lab-PoliInfo-2 Task of NTCIR-15
원문 초록
There are many discussions held during political meetings, and a large number of utterances for various topics is included in their transcripts. We need to read all of them if we want to follow speakers\' intentions or opinions about a given topic. To avoid such a costly and time-consuming process to grasp often longish discussions, NLP researchers work on generating concise summaries of utterances. Summarization subtask in QA Lab-PoliInfo-2 task of the NTCIR-15 addresses this problem for Japanese utterances in assembly minutes, and our team (SKRA) participated in this subtask. As a first step for summarizing utterances, we created a new pre-trained sentence embedding model, i.e. the Japanese Political Sentence-BERT. With this model, we summarize utterances without labelled data. This paper describes our approach to solving the task and discusses its results.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Contrastive Prompting Enhances Sentence Embeddings in LLMs through Inference-Time Steering
Information Leakage of Sentence Embeddings via Generative Embedding Inversion Attacks
IPCGRL: Language-Instructed Reinforcement Learning for Procedural Level Generation
FanChuan: A Multilingual and Graph-Structured Benchmark For Parody Detection and Analysis
Exploring RWKV for Sentence Embeddings: Layer-wise Analysis and Baseline Comparison for Semantic Similarity
Refining Sentence Embedding Model through Ranking Sentences Generation with Large Language Models
토론
이 논문과 연결된 포럼 글이 아직 없습니다.