Unveiling the Art of Heading Design: A Harmonious Blend of Summarization, Neology, and Algorithm

Shaobo Cui, Yiyang Feng, Yisong Mao, Yifan Hou, Boi Faltings


Abstract
Crafting an appealing heading is crucial for attracting readers and marketing work or products. A popular way is to summarize the main idea with a refined description and a memorable acronym. However, there lacks a systematic study and a formal benchmark including datasets and metrics. Motivated by this absence, we introduce LOgogram, a novel benchmark comprising 6,653 paper abstracts with corresponding descriptions and acronyms. To measure the quality of heading generation, we propose a set of evaluation metrics from three aspects: summarization, neology, and algorithm. Additionally, we explore three strategies for heading generation(generation ordering, tokenization of acronyms, and framework design) under various prevalent learning paradigms(supervised fine-tuning, in-context learning with Large Language Models(LLMs), and reinforcement learning) on our benchmark. Our experimental results indicate the difficulty in identifying a practice that excels across all summarization, neologistic, and algorithmic aspects.
Anthology ID:
2024.findings-acl.368
Volume:
Findings of the Association for Computational Linguistics ACL 2024
Month:
August
Year:
2024
Address:
Bangkok, Thailand and virtual meeting
Editors:
Lun-Wei Ku, Andre Martins, Vivek Srikumar
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6149–6174
Language:
URL:
https://aclanthology.org/2024.findings-acl.368
DOI:
Bibkey:
Cite (ACL):
Shaobo Cui, Yiyang Feng, Yisong Mao, Yifan Hou, and Boi Faltings. 2024. Unveiling the Art of Heading Design: A Harmonious Blend of Summarization, Neology, and Algorithm. In Findings of the Association for Computational Linguistics ACL 2024, pages 6149–6174, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.
Cite (Informal):
Unveiling the Art of Heading Design: A Harmonious Blend of Summarization, Neology, and Algorithm (Cui et al., Findings 2024)
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PDF:
https://aclanthology.org/2024.findings-acl.368.pdf