AI Review Summaries in German, French, and Other Languages
How Sumzy reads non-English reviews and writes in your store's output language: what works well for German and French and where honest limits exist.
If your store sells to customers in Germany, France, or anywhere else where English is not the first language, your review corpus is probably a mix. Some buyers write in their own language. Some write in English even when it is not their first. A few write in both, sometimes within the same review.
That mix is not a problem for the AI review summary. The model reads reviews in whatever language they were written and writes the output summary in the language you configure for your store. Here is what that means in practice, where it works well, and where you should set honest expectations.
Can an AI summary read German and French reviews?
Yes. Sumzy reads each review in the language it was written in and writes the summary in the output language you have set for your store, so a corpus that mixes German, French and English still produces one clean summary. German and French are both high-coverage languages for the model, which shows up in the grammar and in the product vocabulary rather than in text that reads like a translation.
How the AI reads non-English reviews
The model that generates your summaries is trained on text in dozens of languages. When a buyer writes a review in German, the model reads it in German. It does not translate the review into English first and then summarise it. It processes the original text, extracts the themes that recur across your review corpus, and then writes the output in your configured output language.
For a store with the output language set to German, a product page with reviews written in a mix of German and English will produce a German-language summary. The themes from the German reviews and the themes from the English reviews are both available to the model, and the summary weighs them proportionally. A complaint that appears in English reviews from buyers in the UK and in German reviews from buyers in Germany will appear in the summary because it is recurring. The language the individual reviewer used does not reduce or increase the weight of the theme.
The output language setting is store-wide on every plan. On Business, you can also enable per-shopper language output with WPML or Polylang, which means a German-speaking shopper sees the summary in German and a French-speaking shopper sees it in French from the same product page. That feature is covered separately in the WPML and WooCommerce review summaries post.
German and French: what works well
German and French are both high-coverage languages in the model. The model has strong capabilities in both, which means a few things for your summaries.
First, the model handles grammatical complexity correctly. German has compound nouns, case declension, and sentence structures that differ significantly from English. French has gendered nouns, complex tense usage, and register differences between formal and informal writing. The model navigates these naturally when writing the output. A German summary does not read like a translated English text.
Second, technical and product-specific vocabulary is generally well-handled. If your German-language reviews use industry-specific terms for materials, fit, or functionality, the model recognises and uses those terms in the output. A fabric review in German that mentions "Stoffqualität" will generate a summary that uses the right vocabulary, not a clunky approximation.
Third, the proportion language that Sumzy uses reads naturally in both German and French: "some buyers mention" or "a few reviewers note." This matters because the proportional framing is the core of honest summarisation. The German and French versions of these phrases are not wooden.
Where edge cases exist in German and French: dialectal variation. Swiss German differs from standard German in ways that can occasionally affect theme extraction from dialect-heavy reviews. Quebec French differs from French in France in vocabulary and phrasing. The model handles mainstream usage well and handles regional varieties reasonably, but if your review corpus is heavily dialectal, some nuance may be reduced in the output.
Right-to-left languages: Arabic and Hebrew
Arabic and Hebrew work differently from European languages in one important way: text direction. The widget handles direction automatically. When the output language is Arabic or Hebrew, the widget renders text right-to-left. You do not configure this separately. The widget detects the text direction from the summary content and sets the dir attribute accordingly.
Arabic is one of the higher-coverage languages in the model. Hebrew has reasonable coverage, though the review corpus in Hebrew tends to be smaller on most WooCommerce stores. The output quality tracks the quality and volume of the input reviews.
One practical note: if your store sells primarily to Arabic-speaking buyers, set your output language to Arabic. The model will then read your reviews in whatever languages they were written and produce an Arabic-language summary. If you are running a WPML or Polylang store and want Arabic-speaking shoppers to see an Arabic summary while German-speaking shoppers see a German one, that is the Business-tier per-shopper feature.
Languages with lower coverage
The model covers a large number of languages but not all languages equally. Major European languages work well: German, French, Spanish, Italian, Dutch, Portuguese, Polish. So do the Scandinavian languages: Swedish, Norwegian, Danish. Japanese and Korean work well. Arabic works well.
Some less widely-spoken European languages, and many languages from smaller regional markets, have lower coverage. The model can still process them and produce output, but the nuance in theme extraction is thinner. If your store sells to buyers who write primarily in, say, Catalan, Serbian, or Slovenian, the summary will be less precise than a German-language summary from the same review volume.
The honest answer about lower-coverage languages is this: the summary will still surface recurring themes, but the granularity of those themes is lower, and the output language quality is lower. If your store's primary market is in a lower-coverage language, it is worth generating a test summary for one of your higher-review-count products and evaluating the output before relying on it across your catalog.
If you have reviews in a language the model handles weakly and the output language is a high-coverage language like English or German, the cross-language summarisation still works. It will extract themes from the lower-coverage reviews at reduced precision and express the output in the high-coverage output language correctly.
Setting your output language
The output language setting is in the Sumzy settings page in your WordPress admin. Sumzy, a WooCommerce AI review summary plugin, reads review text in any language and writes the output in whatever language you have configured. You set it once and it applies to all generated summaries. If you change it later, the Settings page offers to regenerate the summaries you already have, so you can do the whole catalog in one action. If you skip that offer, the change still lands on its own: the output language is part of how Sumzy decides a summary is out of date, so the routine background refresh rewrites your existing summaries in the new language as it works through your catalog.
A few things worth noting about the settings sequence: Sumzy does not generate any summaries until you have reviewed and applied your settings. This includes the output language. The settings-applied gate means you set your language preference first, then the summaries are generated against that preference. If you are running a German-language store, set the output language to German before the first generation runs.
If you have not settled on an approach yet, the three routes to a summary covers what each one asks of you before language even comes up.
The per-shopper use case
On the Business plan with WPML or Polylang, Sumzy generates a separate summary in each shopper's browsing language, with no per-product variant cap. A shopper who arrives in a language your store has not built yet reads the store default summary and gets their own language on a later visit, once the background job has run.
For anything related to per-shopper WPML integration specifically, the WPML and WooCommerce review summaries post goes into how that feature works step by step.
Sumzy's pricing lists the plan that includes per-shopper language output alongside everything else covered in this post.
Help shoppers decide faster
14-day free trial on WooCommerce and Shopify.