How can the summary and conclusion sections in an article be generated through AI?

AI can generate article summaries and conclusions by leveraging advanced natural language processing (NLP) techniques like abstractive and extractive summarization. These systems analyze the full text to identify core arguments, key findings, and their implications, synthesizing a concise overview. While feasible, the output requires careful review and editing to ensure accuracy and alignment with the original work. Several key principles apply. Effective AI summarization relies heavily on the quality, clarity, and structure of the source article. Systems primarily identify the most salient sentences and concepts (extractive summarization) or generate novel phrasing capturing the essence (abstractive summarization). The process necessitates detailed, domain-specific training data and precise prompting. Crucially, the generated sections are derivatives of the input text; AI cannot introduce genuinely new, original conclusions beyond the provided content. Rigorous human oversight is mandatory to verify factual correctness, logical flow, eliminate potential bias or hallucination, and ensure the summary accurately reflects the author's intent and contribution. The application involves preparing the full article text, defining specific requirements for the summary or conclusion's focus and length through prompts, processing it through a specialised AI tool, and critically refining the output. This offers significant value in drafting initial versions and handling large volumes of information efficiently. However, the final output remains a tool-assisted draft; human authors bear responsibility for accuracy, originality, and adherence to ethical standards before inclusion in scholarly work.