Fast generation of abstracts from general domain text corpora by extracting relevant sentences
Open accessAbstract
This paper describes a system for generating text abstracts which relies on a general, purely statistical principle, i.e., on the notion of "relevance", as it is defined in terms of the combination of feild weights of words in a sentence. The system generates abstracts from newspaper articles by selecting the "most relevant" sentences and combining them in text order. Since neither domain knowledge nor text-sort-specific heuristics are involved, this system provides maximal generality and flexibility. Also, it is fast and can be efficiently implemented for both on-line and off-line purposes. An experiment shows that recall and precision for the extracted sentences (taking the sentences extracted by human subjects as a baseline) is within the same range as recall/precision when the human subjects are compared amongst each other: this means in fact that the performance of the system is indistinguishable from the performance of a human abstractor. Finally, the system yields significantly better results than a default "lead" algorithm does which chooses just some initial sentences from the text.
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Variability in human-written summaries can negatively impact the reliability of automated summarization metrics like Rouge.
“As evidenced by earlier work in summarization evaluation (Cohan and Goharian, 2016), variability in human-written summaries (Zechner, 1996; Harman and Over, 2004) can negatively impact the reliability of automated summarization metrics like Rouge (Lin, 2004).”
From TLDR: Extreme Summarization of Scientific Documents (2020) · cites this paper as Zechner, 1996
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Research works printed but not linked: 7
- Brandow, R., Mitze, K., Ibm, L.F. 1995. Auto- mat;it Condensation of Electronic Publications by Se, ntence Selection. In: Information P,,vccss- ing 84 Management, ,71(,5). pp.675 685
- Edmundson, H.P. 1969. New Methods in Au- tomatic Extracting. In: Journal of the ACM, 16(2). pp.264 285
- Hearst, M.A., Plaunt, C. 1993. Subt;opie Sl;ruc- luring for Pull-Length Docmn(mt Access. In: Proceedings of the 16th ACM-SIGII~ Confl;r- trice, t)p.59
- Hobbs, ,I.R., At)I)elt, D.E., Bear, ,1.S., Israel, l).J., Tyson, W.M. 1992. FASTUS: A System for Extra(:ting Int'ormation h'oln Natural Language Text. SRI International, Technical Note 519, Menlo Park, CA Jaeobs, P.S., Rau, L.F. 1990. SCISOR: Extract- ing Informal;ion Dora On-line News. ht: Com- munications of the ACM, .73 (11). pp.88
- Kupiec, J., Pedersen, J., (]hen, F. :1995. A Train- al)le, Docmne.nt Summarizer. In: l'r'oce¢'.dings of the. 18th ACM-SIGIR Confe~w~,cc.. t)p.68
- Mauhlin, M.L. 11989. Information l/.etrieval l)y Texl; Skimming. CMU-CS-89-193, Carnegie Mclhm University, Pittsbllrgh, PA Miike, S., It;oh, E., Ono, K., Sumita, K. 1994. A Full-Text Retrieval System with a Dynamic Abstract; Generation Funct;ion. In: Proceedings of the 17th ACM-SIGIR Um~fl:~v.'nce. t)t).152 161
- Sparck Jones, K., En(lres-Niggemeyer, i3. :1995. Automal;i(: Summarizing. In: Information P~v- cessing I"i Management, 31(5). pp.625 630
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TLDR: Extreme Summarization of Scientific Documents
Cachola, I., Lo, K., Cohan, A., & Weld, D. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020 · doi:10.18653/v1/2020.findings-emnlp.428 · Cited by 169 · see citing works
“3 Most summarization datasets provide a single gold summary for a given document, despite early work in summarization evaluation identifying variety in human generated summaries (Zechner, 1996; Harman and Over, 2004).”
Background
- Authors
- Zechner, K.
- Affiliations
- Carnegie Mellon University (US)
- Corresponding author
- Zechner, Klaus
- ORCID
- Zechner, Klaus
- Venue
- Proceedings of the 16th conference on Computational linguistics -
- Published
- 1996
- Volume
- 2
- Pages
- 986
- ISSN
- 1525-2477 (ISSN-L)
- OpenAlex
- W2171633935
- Cited by
- 107
- References
- 0
- Citations by year
- 2026: 1 · 2025: 1 · 2023: 2 · 2022: 1 · 2021: 1 · 2020: 2 · 2018: 2 · 2016: 1 · 2015: 1 · 2014: 1 · 2012: 7
- FWCI
- 3.76
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Conference on Computational Linguistics (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Text Analysis Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- text summarization, abstract generation, sentence extraction, relevance, word weight, newspaper articles, statistical summarization
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