Terms Derived from Frequent Sequences for Extractive Text Summarization

Abstract

Abstract. Automatic text summarization helps the user to quickly understand large volumes of information. We present a language-and domain-independent statistical-based method for single-document extractive summarization, i.e., to produce a text summary by extracting some sentences from the given text. We show experimentally that words that are parts of bigrams that repeat more than once in the text are good terms to describe the text's contents, and so are also so-called maximal frequent sentences. We also show that the frequency of the term as term weight gives good results (while we only count the occurrences of a term in repeating bigrams).

Graph Ranking on Maximal Frequent Sequences for Single Extractive Text Summarization

“…In [1] was shown that W are better than M for single text extractive summarization using options W, f, best and W, f, 1best+first. Thus, we conclude that the proposed method benefits MFSs (option M).…”

“…One can observe from [1] that any kbest+first sentence selection option not outperformed any combination that used the standard sentence selection scheme, with bigger k always giving better results-that is, only the slightest correction to the baseline deteriorate it. See the comparison of MFS1 and MFS2 [1].…”

“…Relations that connect MFSs are term weighting relations such as (1) frequency of MFSs in a text: f, (2) length of MFS: l, and its presence as 1 or its absence as 0 (see Section 3).…”

“…1 In the example from 1 In practice, we only considered the MFSs with the thresholds β = 2, 3, and 4, since MFSs with higher thresholds were very rare in our collection, except for those generated by stop-words. Section 3, M = {is the most beautiful, the most beautiful}.…”

“…For this, one should decide what the terms are: for example, they can be words; deciding what objects will count as terms is the task of term selection. Different extractive summarization methods can be characterized by how they perform these tasks [1].…”