Unveiling the Art of Heading Design: A Harmonious Blend of Summarization, Neology, and Algorithm (2024)

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.

## introduction heading design has various applications in news recommendation @xcite , academia @xcite , advertising @xcite , and search engine optimization (seo) @xcite . regarding academia, a well-crafted paper heading should accurately encapsulate the paper's main idea while also being memorable and appealing. a typical approach is to concatenate the following two components as the heading @xcite : (i) description -the main body of the heading that summarizes the paper's main idea; the acronym "roberta" forms itself from the underlined letters shown in the description. additionally, it is memorable because it resembles a word and is the name of a character from the television series sesame street. despite growing interest in heading generation, there is still a lack of systematic studies and benchmarks on this topic. addressing this, we propose a fun generation task predicting the description and acronym of the heading given the abstract of a paper as its main idea. this task is challenging as it requires a harmonious blend of multiple constraints: (i) summarization constraint -the predicted description should be a precise summary of the abstract; (ii) neologistic constraint -the predicted acronym should look like a word and be memorable; (iii) algorithmic constraint -the letters in the acronym should be taken sequentially from the words in the description. it is clear from figure 1 that this constraint is satisfied with the aspect (our work) ## task of in this section, we first formally define our task in § 2.1 and compare it with existing relevant tasks in § 2.2. then, we propose three focused research questions that motivate our work in § 2.3. ## automatic evaluation of in this section, we propose automatic evaluation metrics in § 4.1 and discuss their justification evidence in § 4.2. we elaborate on these metrics in detail in app. c. ## empirical study on this section addresses rq iii, which focuses on assessing the effectiveness of various strategies and learning paradigms when applied to language models. we specifically examine their performance on summarization, neologistic capabilities, and algorithmic dimensions. we first introduce several potential strategies in § 5.1. following this, in § 5.2, we demonstrate the usage of these strategies across different learning paradigms. finally, we thoroughly assess the effectiveness of the strategies and learning paradigms across the summarization, neologistic, and algorithmic performance in 5.3. ## conclusions in this paper, we introduce a novel task that aims at generating headings that comprise both descriptions and acronyms, supported by a dedicated dataset, , and tailored evaluation metrics. this task poses challenges in summarization, neologistic, and algorithmic aspects. furthermore, we propose different generation strategies, including generation ordering, tokenization of acronyms, and framework design, and assess their effectiveness across learning paradigms such as supervised fine-tuning, reinforcement learning, and in-context learning with llms. our findings highlight the complexities involved in striking an optimal balance between summarization, neologism, and algorithmic constraints, demonstrating the intricate nature of this task.