ClueGraphSum: Let Key Clues Guide the Cross-Lingual Abstractive Summarization

Abstract

Cross-Lingual Summarization (CLS) is the task to generate a summary in one language for an article in a different language. Previous studies on CLS mainly take pipeline methods or train the end-to-end model using the translated parallel data. However, the quality of generated cross-lingual summaries needs more further efforts to improve, and the model performance has never been evaluated on the hand-written CLS dataset. Therefore, we first propose a clue-guided cross-lingual abstractive summarization method to improve the quality of cross-lingual summaries, and then construct a novel hand-written CLS dataset for evaluation. Specifically, we extract keywords, named entities, etc. of the input article as key clues for summarization and then design a clue-guided algorithm to transform an article into a graph with less noisy sentences. One Graph encoder is built to learn sentence semantics and article structures and one Clue encoder is built to encode and translate key clues, ensuring the information of important parts are reserved in the generated summary. These two encoders are connected by one decoder to directly learn cross-lingual semantics. Experimental results show that our method has stronger robustness for longer inputs and substantially improves the performance over the strong baseline, achieving an improvement of 8.55 ROUGE-1 (English-to-Chinese summarization) and 2.13 MoverScore (Chinese-to-English summarization) scores over the existing SOTA.

A Multitask Cross-Lingual Summary Method Based on ABO Mechanism

“…The method extends the influence of model parameters and weights on generating the final summary. Jiang [24] first extracted key cues, such as keywords and named entities, from the source language text, transformed the source language text into a text graph using a cue-guided algorithm, and then constructed a graph encoder and a cue encoder to encode the text graph and key cues, respectively. The respective outputs were passed into the decoder and finally the output distribution and translation distribution were used together to generate the summary.…”