Sentence Extraction As A Classification Task

A useful first step m document summau-sation is the selection of a small number of 'meamngful' sentences from a larger text Kupiec et al (1995) describe tim as a clas-mficatlon task on the basis of a corpus of technical papers with summaries written by professional abstractors, their system ldent~fies those sentences m the text which also occur in the summary, and then acquires a model of the 'abstract-worthiness' of a sentence as a combination of a hmlted numbel of properties of that sentence We report on a rephcatlon of thin exper-nnent with different data summaries for our documents were not written by professional abstractors, but by the authors themselves Tins produced fewer allguable sentences to tram on We use alternative 'meaningful' sentences (selected by a human judge) as training and evaluation material , because tlns has advantages for the subsequent automatic generation of more flexible abstracts We quantitatively compare the two ¢hfferent strategies for training and evaluation (vm ahgnment vs human judgement), we also chscnss qualitative chf-ferences and consequences for the genera-tlon of abstracts 1 Introduction A useful first step m the automatic or semi-automatic generation of abstracts from source texts m the selection of a small number of 'meamngful' sentences from the source text To achieve tins, each sentence m the source text is scored according to some measure of importance, and the best-rated sentences are selected Thin results m collections of the N most 'meamngful' sentences, m the order m wlnch they appeared m the source text-we will call these excerpts An excerpt can be used to give readers an idea of what the longer text m about, or It can be used as input into a process to .produce a more coherent abstract It has been argued for almost 40 years that it m posmble to automatically create excerpts which meet bamc reformation compresmon needs (Luhn, 1958) Since then, different measurements for the importance of a sentence have been suggested, m particular stochastic measurements for the mgmficance of stressed the Importance of heuristics for the location of the candidate sentence m the source text (Baxen-dale, 1958) and for the occurrence of cue phrases Single heunstms tend to work well on documents that resemble each other m style and content For the more robust creation of excerpts, combinations of these heuristics can be used The eruclal question m how to combine the ¢hfferent heuristics In the past, the …