Prompt-level glossary injection can recover ASR-corrupted domain terms, but its effectiveness depends sharply on glossary scope. In enterprise meeting summarization, a black-box STT system passes only a 1-best transcript to the summarizer; its errors on loanwords, acronyms, and domain terms can replace the canonical forms needed downstream. Motivated by the keyword-input UX of a deployed Korean meeting summarization service, we evaluate training-free prompt-level injection of an annotation-derived meeting-scoped glossary proxy without transcript rewriting. On 30 public Korean meetings processed offline by operational STT, 27% of annotated terms are corrupted under normalized matching, and a no-glossary summarizer recovers only 1.3% of these corrupted terms. Meeting-scoped injection raises term recovery to 53.8%, with 89.2% of output keywords grounded in the transcript. The effect collapses under broader scopes: on the same 45 terms, meeting-level recovery is 66.7% versus 6.7% for a 2,669-term full-dictionary proxy. Coverage-controlled size sweeps show this collapse on Qwen3-8B, and three EXAONE-3.5 points reproduce it, indicating prompt-level over-biasing. A four-annotator human evaluation does not establish improved summary utility; we therefore treat domain-term recovery as an operational dimension, not a summary-preference proxy. We contribute a public-data evaluation protocol and deployment analysis of domain-term recovery under black-box STT constraints.

Nowadays, fake news is spreading in various ways, and this fake information is causing a lot of social damages. Thus the need to detect fake information is increasing to prevent the damages caused by fake news. In this paper, we propose a novel graph-based fake news detection method using a summarization technique that uses only the document internal information. Our proposed method represents the relationship between all sentences using a graph and the reflection rate of contextual information among sentences is computed by using an attention mechanism. In addition, we improve the performance of fake news detection by utilizing summary information as an important subject of the document. The experimental results demonstrate that our method achieves high accuracy, 91.04%, that is 8.85%p better than the previous method.