# FrameSum: Leveraging Framing Theory and Deep Learning for Enhanced News Text Summarization
## 4 Experiments
### News Text Summarization Generation Experiments
#### 4.3.3 Experimental Results and Analysis
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **ROUGE-1** & **ROUGE-2** & **ROUGE-L** \\ \hline Lead & 34.91 & 12.03 & 27.07 \\ LexRank & 32.83 & 10.93 & 25.44 \\ TextRank & 35.01 & 11.51 & 31.42 \\ T5 & 38.98 & 13.07 & 29.34 \\ FT & 40.52 & 14.16 & 29.92 \\ Llama3 & 42.13 & 14.51 & 28.41 \\ Our model & 43.48 & 19.29 & 30.85 \\ (FrameSum) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Experimental results on the XSum dataset. From the figure above, it can be observed that the summaries generated by Qwen have the highest readability, benefiting from its language style optimization function aimed at the general public. However, Qwen's performance in information coverage and framework relevance is average, possibly because it focuses more on the readability of summaries while neglecting information completeness. Llama3's metrics are relatively balanced, but there is still a gap compared to human-written summaries. In contrast, FrameSum is on par with Qwen and Llama3 in terms of readability and information coverage but far surpasses them in framework relevance, achieving a high score of 4.35. This indicates that the summaries generated by FrameSum are not only easy to understand and information rich but also closely aligned with the core themes of news reports, accurately summarizing the central content of the articles. The above results fully validate the positive role of incorporating framing knowledge in improving summary quality. 3. Case Analysis  
To intuitively showcase the strengths and weaknesses of the summaries generated by each model, Table 7 presents a news report along with a manually written reference summary and the summaries generated by each model. From Table 7, it can be observed that the summaries generated by different models exhibit notable differences in content coverage and emphasis. The sentences extracted by Lead are from the beginning of the article, and although they provide background information about the news, they do not cover the subsequent important content, such as the driver being cited. The summaries generated by LexRank and TextRank are concise and cover the core information but lack some important details, such as [PERSON]'s age and her decision not to press charges. The summaries by pointer-generator and GPT-2 are relatively comprehensive and contain most of the key information, but they differ in details, such as whether they mention [PERSON]'s desire to move forward. The summaries by T5 and flat transformer are clear and concise but may overlook some minor details, such as the specific date of the incident. The summaries by Llama3 and Qwen achieve a good balance between information coverage and conciseness, including most of the key information. In contrast, the summary generated by FrameSum not only covers the key elements of the event (such as the individuals involved, age, location, injuries, and follow-up actions) but also captures [PERSON]'s attitude of wanting to move forward. This summary maintains conciseness while accurately grasping the core content of the news. Figure 6: Manual evaluation results.