BibSonomy bookmarks for /tag/summarization

Resources for Abstractive Summarization of Long Documents

schwemmlein — 2021-04-15T11:41:45+02:00

Resources for the NAACL 2018 paper "A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents" - armancohan/long-summarization

Generating Text Summaries Using GPT-2 on PyTorch | Paperspace Blog

festplatte — 2020-10-28T07:36:51+01:00

https://blog.paperspace.com/generating-text-summaries-gpt-2/

gensim/summarization_tutorial.ipynb at develop · RaRe-Technologies/gensim

natm — 2016-11-01T14:10:43+01:00

https://github.com/RaRe-Technologies/gensim/blob/develop/docs/notebooks/summarization_tutorial.ipynb

Generating and Summarizing Explanations for Linked Data

gpublio — 2016-05-25T12:09:42+02:00

http://2014.eswc-conferences.org/sites/default/files/papers/paper_139.pdf

Text Mining, Analytics & More

schwemmlein — 2015-08-17T15:47:44+02:00

http://www.text-analytics101.com/

Document Understanding Conferences

schwemmlein — 2015-08-17T15:44:13+02:00

http://duc.nist.gov/

Kindlers Literatur Lexikon Online

schwemmlein — 2015-07-10T17:27:52+02:00

http://kll-aktuell.cedion.de/nxt/gateway.dll/kll/m/k0488600.xml?f=templates$fn=index.htm$vid=default$3.0

Opinosis Dataset - Topic related review sentences | Kavita Ganesan

schwemmlein — 2015-07-07T13:16:33+02:00

http://kavita-ganesan.com/opinosis-opinion-dataset

ROUGE 2.0 - Java Package for Evaluation of Summarization Tasks

kavitaxx — 2015-03-03T18:00:07+01:00

ROUGE 2.0 is a Java Package for Evaluation of Summarization Tasks building on the Perl Implementation of ROUGE with some updated and improved measures.

Science Gist - Simplifiying science

jaeschke — 2014-03-27T12:51:08+01:00

http://www.sciencegist.com/

A Tokenizer : brendano/tweetmotif

becker — 2012-08-03T16:06:13+02:00

https://github.com/brendano/tweetmotif

Opinosis Opinion & Text Summarization Software

dollyk — 2010-10-16T09:58:47+02:00

This is an abstractive summarization demo program. It was mainly used to summarize opinions, but since it does not rely on any domain information, it can be used to summarize any highly redundant text.

Opinosis: A Graph Based Approach to Abstractive Summarization of Highly Redundant Opinions

dollyk — 2010-09-28T06:29:01+02:00

This paper presents a flexible framework for generating very short abstractive summaries. The key idea is to use a word graph data structure referred to as the Opinosis-Graph to represent the text to be summarized. Then, we repeatedly find paths through this graph to produce concise summaries. We consider Opinosis a "shallow" abstractive summarizer as it uses the original text itself to generate summaries. This is unlike a true abstractive summarizer that would need a deeper level of natural language understanding. While the evaluation is on an opinion dataset, the approach itself is general in that, it can be applied to any corpus containing high amounts of redundancies, for example, Twitter comments or user comments on blog/news articles. A very similar work to ours (published at the same time and at the same conference) is the following: Multi-sentence compression: Finding shortest paths in word graphs Proceedings of the 23rd International Conference on Computaional Linguistics (COLING 10). Beijing, China, August 23-27, 2010. Katja Filippova Katja's work was evaluated on a news dataset (google news) for both English and Spanish while ours was evaluated on user reviews from various sources (English only). She studies the informativeness and grammaticality of sentences and in a similar way we evaluate these aspects by studying how close the Opinosis summaries are compared to the human composed summaries in terms of information overlap and readability (using a human assessor).

BibSonomy::user::zhouwen

zhouwen — 2007-03-01T15:22:43+01:00

http://www.bibsonomy.org/user/zhouwen

Dragomir R. Radev

schmitz — 2006-09-06T10:34:37+02:00

http://tangra.si.umich.edu/~radev/

Google News Deutschland

hotho — 2006-01-23T08:09:03+01:00

http://news.google.de/

Document Understanding Conferences

schmitz — 2005-09-19T12:08:03+02:00

http://www-nlpir.nist.gov/projects/duc/index.html

Text Summarization

hotho — 2005-07-13T11:47:32+02:00

http://www.summarization.com/