## 2 Related Works  
### Abstractive Text Summarization  
The advancement of ATS technology facilitates the generation of concise and coherent summaries that effectively address information overload [13; 14]. Since 2020, Transformer models and their variants, such as GPT, BERT, and BART, have demonstrated remarkable performance in NLP, particularly in generative tasks [3]. Unlike extractive summarization, which directly extracts information from the source text, abstractive summarization requires a deeper understanding of textual semantics and employs NLG algorithms to rephrase critical points [15]. This approach makes ABS more similar to human-generated summarization and significantly improves fluency and readability [16]. The complicated generation process, however, presents a prominent challenge: factual accuracy. During summary generation, errors in entity replacement or inaccuracies in logical relationships are expected. Such factual errors are challenging to detect or rectify using current statistical metrics [17]. Defining \"what is a fact\" is a crucial foundation for addressing the issue of factual accuracy, yet no unified definition for it currently exists. Before 2020, most studies regarded relational triples as the fundamental form of facts, using them to improve the factual awareness of language models and precision. However, comprehensive factual triples are not always extractable. Existing research often involves the introduction of numerous auxiliary virtual entities and additional triples, conversions that contribute to the complexity of predicting links for two or more \"arcs\" [18]. In recent years, broader dimensions of fact definitions have been proposed. Some researchers defined facts based on the keywords in the original text, such as the TF-IDF method [19]. This approach is essentially statistical, assessing the significance of a word in a document or corpus, enabling an intuitive identification of the text's theme. However, such statistical methods fail to comprehend the relations between identified words and others. Part-of-speech tagging has also been utilized for fact extraction, assigning specific functional labels to each word in a text, such as nouns, verbs, or adjectives [20]. Different parts of speech may represent various factual elements and tagging them helps identify their specific roles in sentences, which applies to texts of diverse types and styles. Nonetheless, this method might be overly simplistic, potentially overlooking more intricate semantic relationships. Entity recognition [17] has also been employed for fact extraction, discerning items with specific meanings from the text, and viewing entities as facts, proving valid in downstream tasks like text summarization and information retrieval with minimal cumulative error. However, entity recognition might neglect important information unrelated to entities. Some researchers employ dependency relations as a fact extraction method to uncover intricate relations between text lexemes. Dependency relations aid in understanding the primary structure of texts, transforming input sentences into labeled tuples, and extracting tuples associated with predicate lexemes [21]. However, these relations mainly capture inter-word relationships. In complex sentences, if the dependency path between two words is too lengthy or contains numerous nodes, the facts derived from these paths might fail to fully represent the sentence's intended meaning. Understanding the characteristics of human linguistic cognition is of paramount importance. Some researchers have significantly enhanced the performance of ABS models by simulating features of human linguistic cognition [22]. When humans read and comprehend texts, they typically rely on various cues to discern which information is pivotal or significant. These cues often stem from individuals' expectations and knowledge background, bearing a certain degree of subjectivity, and might operate subconsciously, making them challenging to emulate directly through computational models.