Topic Modeling场景下短文本的定义及论文标题摘要判定咨询

Great question—this is actually a super common point of confusion in topic modeling, since there’s no universal hard-and-fast rule for what counts as "short text" here. But let’s break it down based on common practices in the field:

General Definition of Short Text for Topic Modeling #

• At its core, short text in this context is defined by limited vocabulary size and high semantic density with minimal contextual redundancy. There’s no strict word count threshold, but most researchers draw the line somewhere between a handful of words and 500 words.
• The key distinction isn’t just the number of words—it’s whether the text provides enough contextual clues for traditional topic models (like LDA) to infer stable topic distributions. Short texts suffer heavily from the curse of dimensionality: with so few words, individual documents rarely contain enough overlapping terms to let models confidently map them to coherent topics.

Do Academic Paper Titles & Abstracts Qualify? #

Let’s tackle each one separately:

• Paper Titles: Absolutely. Titles are the epitome of short text—they’re usually 5-30 words, hyper-concentrated to capture the core of the research, and lack the contextual breadth needed for traditional topic models to work well out of the box.
• Paper Abstracts: This depends a bit on length, but most standard abstracts (150-300 words) fall into the short text category. While they’re longer than titles, they’re still far shorter than full papers, and their tight, focused structure means they still struggle with the same sparsity issues that plague shorter texts. If you’re working with extra-long abstracts (500+ words), some might classify them as "medium-length," but they’ll still require adjustments compared to full-document topic modeling.

Quick Tips for Topic Modeling on These Texts #

If you’re planning to model titles or abstracts, keep these in mind:

• Skip traditional bag-of-words models like vanilla LDA—opt for modern alternatives built for short text, such as BERTopic or contextual embedding-based models.
• Be cautious with stopword removal: in short texts, every word carries meaningful weight, so don’t strip out too many terms unless they’re truly irrelevant.
• Consider combining titles and abstracts into a single document per paper—this adds a bit more context and reduces sparsity without losing the focused nature of the content.

内容的提问来源于stack exchange，提问作者Sri Test