Details
| Original language | English |
|---|---|
| Title of host publication | WWW '20: Companion Proceedings of the Web Conference 2020 |
| Publisher | Association for Computing Machinery (ACM) |
| Pages | 226-229 |
| Number of pages | 4 |
| ISBN (electronic) | 9781450370240 |
| Publication status | Published - 20 Apr 2020 |
| Event | 29th International World Wide Web Conference, WWW 2020 - Taipei, Taiwan Duration: 20 Apr 2020 → 24 Apr 2020 |
Abstract
The extraction of main content from web pages is an important task for numerous applications, ranging from usability aspects, like reader views for news articles in web browsers, to information retrieval or natural language processing. Existing approaches are lacking as they rely on large amounts of hand-crafted features for classification. This results in models that are tailored to a specific distribution of web pages, e.g. from a certain time frame, but lack in generalization power. We propose a neural sequence labeling model that does not rely on any hand-crafted features but takes only the HTML tags and words that appear in a web page as input. This allows us to present a browser extension which highlights the content of arbitrary web pages directly within the browser using our model. In addition, we create a new, more current dataset to show that our model is able to adapt to changes in the structure of web pages and outperform the state-of-the-art model.
ASJC Scopus subject areas
- Computer Science(all)
- Computer Networks and Communications
- Computer Science(all)
- Software
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WWW '20: Companion Proceedings of the Web Conference 2020. Association for Computing Machinery (ACM), 2020. p. 226-229.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Boilerplate Removal using a Neural Sequence Labeling Model
AU - Leonhardt, Jurek
AU - Anand, Avishek
AU - Khosla, Megha
N1 - Funding information: This work is partially funded by SoBigData++ (EU Horizon 2020 grant agreement no. 871042).
PY - 2020/4/20
Y1 - 2020/4/20
N2 - The extraction of main content from web pages is an important task for numerous applications, ranging from usability aspects, like reader views for news articles in web browsers, to information retrieval or natural language processing. Existing approaches are lacking as they rely on large amounts of hand-crafted features for classification. This results in models that are tailored to a specific distribution of web pages, e.g. from a certain time frame, but lack in generalization power. We propose a neural sequence labeling model that does not rely on any hand-crafted features but takes only the HTML tags and words that appear in a web page as input. This allows us to present a browser extension which highlights the content of arbitrary web pages directly within the browser using our model. In addition, we create a new, more current dataset to show that our model is able to adapt to changes in the structure of web pages and outperform the state-of-the-art model.
AB - The extraction of main content from web pages is an important task for numerous applications, ranging from usability aspects, like reader views for news articles in web browsers, to information retrieval or natural language processing. Existing approaches are lacking as they rely on large amounts of hand-crafted features for classification. This results in models that are tailored to a specific distribution of web pages, e.g. from a certain time frame, but lack in generalization power. We propose a neural sequence labeling model that does not rely on any hand-crafted features but takes only the HTML tags and words that appear in a web page as input. This allows us to present a browser extension which highlights the content of arbitrary web pages directly within the browser using our model. In addition, we create a new, more current dataset to show that our model is able to adapt to changes in the structure of web pages and outperform the state-of-the-art model.
UR - http://www.scopus.com/inward/record.url?scp=85091697325&partnerID=8YFLogxK
U2 - 10.1145/3366424.3383547
DO - 10.1145/3366424.3383547
M3 - Conference contribution
AN - SCOPUS:85091697325
SP - 226
EP - 229
BT - WWW '20: Companion Proceedings of the Web Conference 2020
PB - Association for Computing Machinery (ACM)
T2 - 29th International World Wide Web Conference, WWW 2020
Y2 - 20 April 2020 through 24 April 2020
ER -