Abstractive Summarization of Spoken and Written Conversation

By Prakhar Ganesh, Saket Dingliwal. 2019

11 citations recorded by the source corpus.

Nowadays, lots of information is available in form of dialogues. We propose a novel abstractive summarization system for conversations. We use sequence tagging of utterances for identifying the discourse relations of the dialogue. After aptly capturing these relations in a paragraph, we feed it into an Attention-based pointer network to produce abstractive summaries. We obtain ROUGE-1, 2 F-scores similar to those of extractive summaries of various previous works.

Subfields: Artificial Intelligence, Summarization, Speech & Audio

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Iwona Mochol
2. Aleksander Wawer
3. Bogdan Gliwa
4. Maciej Biesek
5. Yang Liu
6. Xiaojun Wan
7. Mingqi Gao
8. Mihai Surdeanu

In Summarization

1. Iwona Mochol
2. Aleksander Wawer
3. Bogdan Gliwa
4. Maciej Biesek

Citations in the corpus

• SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization (2019, background): “To benefit from large news corpora, Ganesh and Dingliwal (2019) built a dialogue summarization model that first converts a conversation into a structured text document and later applies an attention-based pointer network to create an abstractive summary.”