Towards Adapting Automatic Text Summarization for L2 Learners: Does Compression Rate Affect Summary Readability?

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This paper addresses the problem of readability of automatically generated summaries in the context of second language learning. For this we experimented with a new corpus of level-annotated simplified English texts. The texts were summarized using a total of 7 extractive and abstractive summarization systems with compression rates of 20%, 40%, 60% and 80%. We analyzed the generated summaries in terms of lexical, syntactic and length-based features of readability, and concluded that summary complexity depends on the compression rate, summarization technique and the nature of the summarized corpus. Our experiments demonstrate the importance of choosing appropriate summarization techniques that align with users needs and language proficiency.

Revista: RANLP 2019

Autores: Tatiana Vodolazova and Elena Lloret

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