Open Source Text Processing Project: summarizer

Deep Learning Specialization on Coursera

summarizer: A multidocument text summarizer

Project Website: None

Github Link: https://github.com/kylehg/summarizer

Description

UNMAINTAINED: CIS-530 Final Project
NOTE: This was a school project. It is very likely riddled with bugs, and is entirely unmaintained. It should not be considered for any real-world use and we are not accepting issues or PRs.

A multidocument summarizer. Final project for CIS-530. By Kyle Hardgrave and Amalia Hawkins

Centrality Summarizer

File: centrality.py Main function: gen_centrality_summary(orig_sents, max_words) Summaries: rouge/centrality/ ROUGE config: rouge/centrality-config.xml

Parameters

Valid sentance length: We only chose sentences of between 10 and 55 words. Initially starting with just 15-35, as the write-up recommended, we expanded it slightly to include for the sometimes wordy but information-packed sentences of news articles.

Remove redundant sentences: Using TF-IDF cosine similarity (which we found to be a slightly better indicator than binary cosine similarity), we removed sentences that had a similarity score of greater than 0.4.

Rougue

———————————————
centrality ROUGE-1 Average_R: 0.36114 (95%-conf.int. 0.34785 – 0.37332)
centrality ROUGE-1 Average_P: 0.36661 (95%-conf.int. 0.35374 – 0.37881)
centrality ROUGE-1 Average_F: 0.36369 (95%-conf.int. 0.35107 – 0.37603)
———————————————
centrality ROUGE-2 Average_R: 0.07496 (95%-conf.int. 0.06714 – 0.08299)
centrality ROUGE-2 Average_P: 0.07595 (95%-conf.int. 0.06828 – 0.08400)
centrality ROUGE-2 Average_F: 0.07542 (95%-conf.int. 0.06767 – 0.08341)
Centrality Summarizer

File: lexrank.py Main function: gen_lexrank_summary(orig_sents, max_words) Summaries: rouge/lexrank/ ROUGE config: rouge/lexrank-config.xml

Parameters

Valid sentance length: Again, we only chose sentences of between 10 and 55 words.

Remove redundant sentences: Similar again to centrality, we removed sentences that had a similarity score of greater than 0.4.

Dampening factor: We used a dampening factor of 0.85 in the PageRank algorithm, based on general recommendations for the PageRank algorithm.

Min similarity for edges: Here, we included an edge between any sentence with a TF-IDF cosine similarity of greater than 0.2.

Rougue

———————————————
lexrank ROUGE-1 Average_R: 0.34099 (95%-conf.int. 0.32894 – 0.35323)
lexrank ROUGE-1 Average_P: 0.34600 (95%-conf.int. 0.33375 – 0.35765)
lexrank ROUGE-1 Average_F: 0.34338 (95%-conf.int. 0.33103 – 0.35531)
———————————————
lexrank ROUGE-2 Average_R: 0.06593 (95%-conf.int. 0.05933 – 0.07278)
lexrank ROUGE-2 Average_P: 0.06695 (95%-conf.int. 0.06024 – 0.07405)
lexrank ROUGE-2 Average_F: 0.06642 (95%-conf.int. 0.05971 – 0.07336)


Leave a Reply

Your email address will not be published. Required fields are marked *