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Turkish text summarization - Start generating your online Turkish summary
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Most automatic summarization research has been done on English, and methods that work well there often underperform on Turkish. The reasons are structural, and they are worth understanding if you work with Turkish text.
Turkish builds words by attaching suffixes to a root, and it does so freely. From the root ev ("house") you get evde, evler, evlerimizin, evlerimizden. A frequency-based method that treats surface forms as distinct words will count these as five unrelated terms and miss that they all point to the same concept.
This is why morphological analysis comes first here. Words are reduced to their roots before chains are built, so related forms are recognized as the same underlying term.
Turkish suffixes change shape to harmonize with the vowels of the root. The plural suffix is -ler in some words and -lar in others; the same suffix has several written forms. Rule sets written for a language with fixed affixes cannot account for this.
Turkish permits considerable variation in constituent order — subject, object and verb can be rearranged without changing the meaning. Methods that rely on positional cues, such as assuming the topic appears early in the sentence, are less dependable here. A frequency-and-relatedness approach like lexical chaining is largely order-independent, which suits the language well.
Many Turkish roots serve as both noun and verb. Yaz means "summer" and also "write". Disambiguating requires looking at the suffixes attached and the surrounding words — a step that adds complexity absent in languages where the distinction is clearer.
Expository writing — news, academic articles, reports, lecture notes. These repeat their key terms consistently, which is what the algorithm relies on.
Narrative fiction and poetry. Both carry meaning through structure and imagery rather than term repetition, so a frequency-based method has less to work with.