{"id":9206,"date":"2026-10-07T17:18:23","date_gmt":"2026-10-07T11:48:23","guid":{"rendered":"https:\/\/newsdata.io\/blog\/?p=9206"},"modified":"2026-10-07T17:18:23","modified_gmt":"2026-10-07T11:48:23","slug":"summarize-news-articles-with-an-llm-api-in-python","status":"publish","type":"post","link":"https:\/\/newsdata.io\/blog\/summarize-news-articles-with-an-llm-api-in-python\/","title":{"rendered":"How to Summarize News Articles With an LLM API in Python"},"content":{"rendered":"[vc_row type=”in_container” full_screen_row_position=”middle” column_margin=”default” column_direction=”default” column_direction_tablet=”default” column_direction_phone=”default” scene_position=”center” text_color=”dark” text_align=”left” row_border_radius=”none” row_border_radius_applies=”bg” overflow=”visible” overlay_strength=”0.3″ gradient_direction=”left_to_right” shape_divider_position=”bottom” bg_image_animation=”none”][vc_column column_padding=”no-extra-padding” column_padding_tablet=”inherit” column_padding_phone=”inherit” column_padding_position=”all” column_element_direction_desktop=”default” column_element_spacing=”default” desktop_text_alignment=”default” tablet_text_alignment=”default” phone_text_alignment=”default” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_backdrop_filter=”none” column_shadow=”none” column_border_radius=”none” column_link_target=”_self” column_position=”default” gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”1\/4″ tablet_width_inherit=”default” animation_type=”default” bg_image_animation=”none” border_type=”simple” column_border_width=”none” column_border_style=”solid” column_padding_type=”default” gradient_type=”default” offset=”vc_hidden-sm vc_hidden-xs”][\/vc_column][vc_column column_padding=”no-extra-padding” column_padding_tablet=”inherit” column_padding_phone=”inherit” column_padding_position=”all” column_element_direction_desktop=”default” column_element_spacing=”default” desktop_text_alignment=”default” tablet_text_alignment=”default” phone_text_alignment=”default” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_backdrop_filter=”none” column_shadow=”none” column_border_radius=”none” column_link_target=”_self” column_position=”default” el_class=”text_block_wrapper” gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”3\/4″ tablet_width_inherit=”default” animation_type=”default” bg_image_animation=”none” border_type=”simple” column_border_width=”none” column_border_style=”solid” column_padding_type=”default” gradient_type=”default” offset=”vc_col-lg-9 vc_col-md-12″][image_with_animation image_url=”399″ image_size=”full” animation_type=”entrance” animation=”None” animation_movement_type=”transform_y” hover_animation=”none” alignment=”” border_radius=”none” box_shadow=”none” image_loading=”default” max_width=”100%” max_width_mobile=”default”][vc_column_text]To summarize news articles with an LLM API in Python, request recent articles from the NewsData.io latest endpoint, send each title and description to a chat completions endpoint with a short instruction, and print the reply. This tutorial builds that script with DeepSeek V4 Pro on AI\/ML API<\/a> in 51 lines.<\/p>\n

The script needs only the requests library. Each step below comes with a screenshot of the code and a screenshot of a real run, so you can repeat every action on your own machine.[\/vc_column_text][vc_column_text]\n

How the pipeline works<\/b><\/h2>\n

The script makes two kinds of HTTP calls. A GET request to https:\/\/newsdata.io\/api\/1\/latest returns a JSON object with a results list. Each item in the list is one article with the fields title, description, link, source_id and pubDate. A POST request to https:\/\/api.aimlapi.com\/v1\/chat\/completions sends the article text to the model and returns the summary in choices[0].message.content.<\/p>\n

The script filters out articles that have no description, because there is nothing to summarize in them. It then sends one request per article, so a failed request affects one summary and not the whole batch.<\/p>\n

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The four stages of the script.<\/em><\/p>\n[\/vc_column_text][vc_column_text]\n

What you need before you start<\/b><\/h2>\n

You need four things.<\/p>\n