What Makes it Difficult to Understand a Scientific Literature?

TL;DR

This paper investigates the challenges humans face in understanding scientific literature by analyzing reading comprehension obstacles through semantic link models, aiming to improve AI models for better scientific text understanding.

Contribution

It introduces a semantic link network approach to analyze comprehension difficulties in scientific texts, providing insights for designing more effective AI reading models.

Findings

Identified characteristics of comprehension obstacles in scientific literature.

Analyzed differences in understanding based on knowledge levels.

Proposed semantic link models to represent comprehension challenges.

Abstract

In the artificial intelligence area, one of the ultimate goals is to make computers understand human language and offer assistance. In order to achieve this ideal, researchers of computer science have put forward a lot of models and algorithms attempting at enabling the machine to analyze and process human natural language on different levels of semantics. Although recent progress in this field offers much hope, we still have to ask whether current research can provide assistance that people really desire in reading and comprehension. To this end, we conducted a reading comprehension test on two scientific papers which are written in different styles. We use the semantic link models to analyze the understanding obstacles that people will face in the process of reading and figure out what makes it difficult for human to understand a scientific literature. Through such analysis, we summarized some characteristics and problems which are reflected by people with different levels of knowledge on the comprehension of difficult science and technology literature, which can be modeled in semantic link network. We believe that these characteristics and problems will help us re-examine the existing machine models and are helpful in the designing of new one.