The writer is selective, avoiding trivia, and choosing details that keep the readers reading.
The amount of detail is just right–not skimpy, not overwhelming
- Ideas and Development trait (Section2.1) An article is verbose when it contains unnecessary details which make the article longer than it needs to be. Such writing is unpleasing to readers. Articles that contain too much general content are also of lower quality. Overly general information conveys less meaning. So even after reading a long, very general article, a reader does not obtain much useful detail. In contrast to verbose and overly general articles, concise articles contain the right amount of detail and details which are most necessary for a reader to know. The two definitions at the beginning of this section are taken from the Ideas and Development category of the Six Traits model and emphasize exactly this quality aspect. In this chapter, we develop a computational method to predict verbosity and test its usefulness for making assessments of text quality.
The simple definition of verbosity is “too many words than necessary”. But in a more specific sense, verbosity arises when any or both of the following factors are present (based on definitions from Williams (1990) [170]):
Redundant information: For example, in the phrase “during that period of time”, the use
“during that time” or “during that period”. Similarly, “terrible tragedy” can be shortened as “tragedy”. This type of verbosity arises from excessive use of modifiers, complicated words and clich´e phrases (eg. “each and every”). Such texts can be rewritten into concise ones such as the above examples without loss of information.
Irrelevant details: are those which the reader can infer easily and so they need not be
explicit in the text. Consider the following verbose passage and its simpler concise version taken from Williams (1990)[170].
A. Baseball, one of our oldest and most popular outdoor summer sports in terms of total attendance at ball parks and viewing on television, has the kind of rhythm of play on the field that alternates between the players’ passively waiting with no action taking place between the pitches to the batter and exploding into action when the batter hits a pitched ball to one of the players and he fields it.
B. Baseball has a rhythm that alternates between waiting and explosive action.
Text A is filled with unnecessary detail, for example, in the clauses, “play on the field” and “when the batter hits a pitched ball to one of the players and he fields it”. In addition, depending on the reader, several other pieces of information such as the note about oldest and popular outdoor sport will also become unncessary. The rewritten text is an example concise version of the same content and brings out the main substance of the sentence. In the case of redundant information category, we can create a concise version without losing any detail whereas here, rewriting the text concisely involves conveying less information. But the content that is ignored is not important for the author’s point.
The problem of verbosity in writing is often discussed in writing advice books but there have been no previous attempts to automatically predict verbosity. The work re- ported in this chapter is one of the first studies to propose a measurable indicator of verbosity.
This line of research is related to text specificity which we discussed in Chapter5. In that study, we distinguished between two categories, more detail (specific) and less detail (general). Using the confidence values from the classifier, we developed a measure to indicate the level of specificity for a text. We showed in our experiments that for the task of summarization, automatic summaries with greater general content had better scores
during evaluation by human judges. Summaries with lesser general content scored lower. Similarly, when comparing articles from our science journalism corpus, the better articles according to our gold standard quality categories were those with greater general content compared to the typical articles. However, in that work, we did not examine whether the level of specificity is appropriate for individual texts. In other words, we did not examine whether the provided details are the right amount and most needed given the article’s length. Our idea of verbosity prediction is designed to provide a way to check for the fit of details presented with the article length.
Our approach involves two factors—content type and article length. Specifically, we assume that certain content types are appropriate to be included in a text for a given length and some other content types are excessive and unnecessary detail. We utilize a collection of concise articles and learn a relationship between surface properties of the text (focusing on those which can indicate content type) and the length of the article in words. These properties include level of description approximated by phrase lengths and syntactic form, indications of semantic content by identifying which discourse relations are present, tracking amount of discussion on subtopics using continuity features, and external information about content that is considered important by people. This model captures which type of content and writing is ideal for long and short articles.
During testing, we analyze whether our model identifies the content type in the article as appropriate given the article length. Deviations from concise style will be reflected by a mismatch between the content type and length. We hypothesize that such mismatched articles will have lower quality compared to those where the content type and length have a good relationship when examined under the model of concise writing. Sections 7.1 to
7.5 describe our approach and how we implemented the model using a corpus of news summaries and news articles.
Similar to text specificity, we expected that this metric will be relevant for text quality prediction for automatic summaries. Since a target word limit is given for summary creation, a summarization system needs to decide how much detail to provide so that the summary will be perceived as contentful while at the same time not involving unncessary details. We show that our model is predictive of both content and linguistic quality ratings
for automatic summaries (Section7.7). We also used features from our verbosity model for predicting the quality of science journalism articles. However, we did not gain any performance improvement above baseline for this task (Section7.8).