Paper Summary: A New Probabilistic Model for Title Generation

Paper Summary: A New Probabilistic Model for Title Generation

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Please note This post is mainly intended for my personal use. It is not peer-reviewed work and should not be taken as such.


A framework for creating titles for documents, following different strategies than those found in earlier (as of 2002) articles.


Because they find 2 problems in previous solutions:

  • Using a language model to measure the syntatic correctness of a given word sequence gives too much weight to common words (e.g. pronouns), which are rarely words that capture the semantics of a document.

  • Using the whole document to select words from wrongly assumes that all parts of the document are equally relevant in determining the overall theme of a document.


They assume the existence of a hidden state1 called an information source from which both the document itself and the title sample from. These models are estimated assuming independence between parameters.


  • Previous (as of 2002) approaches for text generation follow a 2-phase process:

    • 1) Word selection phase
    • 2) Word ordering phase (of words selected in step 1)
  • Authors claim the new model beats the old model (no hidden state) with respect to both objective and subjective evaluation metrics.

results-of-new-model-vs-original-titles Selected pairs of original/automatically-
generated titles.
Source: Article


  • Results are measured using an objective measure (F-score) between the original text and the generated texts but also a subjective measure is used whereby human subjectives are asked to rate the generated titles in a scale of 1-5 according to how fitting they are.

  • A word's TF-IDF score is used as a proxy for that word's rarity.

  • The author's model the relationship between a document and its title as a translation from a source, verbose language into a target, more concise language.

  • Authors affirm that there is a difference between title generation and other similar tasks like automatic text summarization and keyword extraction.

1: As in Hidden Markov Models, for instance.


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