Zavtra.ru - Summary of articles
Explore the summary of a sample of articles by year
The following pages include an LLM-generated English language summary of a sample of articles for each year (10% of all articles published, or at least 100 items).
Why LLM-generated summaries
For readers unfamiliar with Zavtra, especially those who do not read Russian, it may be difficult to get a first impression of the thematic focus of the articles and the writing style. The fact that titles of publications are often not descriptive, makes it more difficult to skim through them and get a feeling of the thematic priorities of the editors, as well as the tone and style characteristic of the magazine.
An English-language collection of pre-compiled LLM-generated summaries of a random sample of articles makes it easy to skim through the contents and get a meaningful first impression. Even if the dull style of LLM-generated articles does not fully convey the stylistic verve or the scathing or sarcastic criticism of public figures often found in Zavtra articles, it does offer an meaningful overview of both contents and style. It also allows to get a glimpse of the thematic priorities, including a long-term preoccupation with Russia (as a nation, understood in civilisational terms, more than short-term issues), the prevalence of opinion pieces, the attention to the arts, etc.
As is characteristic of LLMs, these summaries may include inaccuracies.
About the process
Summaries have been generated with the following tools:
- a locally-deployed version of the LLM
gemma3, in its 4b parameters version.gemma3is an open-weights model released by Google Deepmind. ollama, to run the model locallyquackingllama- an R package created by this author, which facilitates systematic processing of texts and local caching
In brief, each of the articles included in the sample has been passed to the LLM, along with a prompt requesting a summary of the text and a translation of the title (see below for details on the prompts). The output is then systematically included in the set of pages divided by year listed above.
The code used to generated them is available on GitHub.
The whole process has been conducted on the author’s own device, without sending data to third parties.
gemma3:4b has been pragmatically chosen as a compromise solution offering meaningful outputs in spite of its relatively limited computational requirements. The same approach could be replicated with other models.
Future versions may include additional data points tentatively extracted through LLMs, possibly including named entities such as people and locations.
About the corpus
These summaries are based on a corpus of all articles published by Zavtra on its official website. Find out more about the corpus and download it from its official release on Discuss Data, its release page on this website, or the unstable version on this website (may be more up to date, but not checked for consistency).
Finally, it should be possible to explore the dataset through an interactive web interface.
Prompt used for summaries
System prompt:
You are a helpful assistant. You summarise in English the text you receive. Your reply must always be in English and include only the summary of the received text, without additional comments or queries.
Message prompt:
Without any additional comment or query, summarise in English the following text: [Full text of the original article here] “End of the text. Summarise in English.”
Prompt used for translating titles
Same system prompt as above. Message prompt:
Without any additional comment or request, translate in English the following text: