Automatic Text Summarization - Znalosti 2008

More documents

Recommendations

Info

Indicative◦ indicates types of information◦ alerts the user to decide whether the document is worth reading◦ commonly used as a backend of IRInformative◦ includes quantitative / qualitative information◦ substitutes full document retaining important details◦ informs the user, while reducing information volumeCritic / evaluative◦ capture the point of view of the summary author (e.g reviewer)Automatic Text Summarization: Ježek and Steinberger, Znalosti 2008, Bratislava6
Relationship between summariescriticalinformativeindicativeAutomatic Text Summarization: Ježek and Steinberger, Znalosti 2008, Bratislava7
Page 1: Karel Ježek & Josef SteinbergerWBU
Page 4 and 5: Definitions (one from many):◦ Bri
Page 8 and 9: Extracts◦ completely consisting o
Page 10 and 11: Generic◦ are aimed at usually bro
Page 12 and 13: Stages of human abstracting◦ Docu
Page 14 and 15: Sentence human computercomputerhuma
Page 16 and 17: The first attempts◦ Luhn◦ Edmun
Page 19 and 20: Important information occurs in spe
Page 21 and 22: TextRank algorithm exploits the str
Page 23 and 24: Features: F1-heading words, F2-cue
Page 25 and 26: LexRank | TextRank both use the Pag
Page 27 and 28: “ PRIEST IS CHARGED WITH POPE ATT
Page 29 and 30: Terms x sentences in the case of su
Page 31 and 32: For each row in V T (topic), choose
Page 33 and 34: N best sentences extracted from eac
Page 35 and 36: Multi-document - input is not a sin
Page 37 and 38: Matrix LSA uses just information ab
Page 39 and 40: termsdocumentssentencesA=termstdimU
Page 41 and 42: N-gram comparison of human written
Page 43: Evaluation and the own summarizatio

Automatic Text Summarization - Znalosti 2008

Create successful ePaper yourself

Delete template?

Save as template?