Abstract
Summarization techniques strive to create a concise summary that conveys the essential information from a given document. However, these techniques are often inadequate for summarizing longer documents containing multiple pages of semantically complex content with various topics. Hence, in this work, we present a Topic-Conditional Summarization (TCS) method, that produces different summaries each conforming to a different topic. TCS is an unsupervised method and does not require ground truth summaries. The proposed algorithm adapts the TextRank paradigm and enhances it with a language model specialized in a set of documents and their topics. Extensive evaluations across multiple datasets indicate that our method improves upon other alternatives by a sizeable margin.
| Original language | English |
|---|---|
| Title of host publication | ICASSP 2024 |
| Subtitle of host publication | 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
| Pages | 11286-11290 |
| Number of pages | 5 |
| DOIs | |
| State | Published - 2024 |
Bibliographical note
DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.Keywords
- Adaptation models
- Signal processing algorithms
- Signal processing ,
- Acoustics
- Speech processing
- Extractive Summarization