Question: Why are current ELS's not sufficiently effective?

Evidence:
raw_evidence: Despite its usefulness, linked entities extracted from ELS's have issues because of low precision rates BIBREF11 and design challenges in training datasets BIBREF12 . These issues can be summarized into two parts: ambiguity and coarseness.
First, the extracted entities may be ambiguous. In the example, the entity “South Korean” is ambiguous because it can refer to both the South Korean person and the South Korean language, among others. In our experimental data, we extract (1) the top 100 entities based on frequency, and (2) the entities extracted from 100 randomly selected texts, and check whether they have disambiguation pages in Wikipedia or not. We discover that $71.0\%$ of the top 100 entities and $53.6\%$ of the entities picked at random have disambiguation pages, which shows that most entities are prone to ambiguity problems.
Second, the linked entities may also be too common to be considered an entity. This may introduce errors and irrelevance to the summary. In the example, “Wednesday” is erroneous because it is wrongly linked to the entity “Wednesday Night Baseball”. Also, “swap” is irrelevant because although it is linked correctly to the entity “Trade (Sports)”, it is too common and irrelevant when generating the summaries. In our experimental data, we randomly select 100 data instances and tag the correctness and relevance of extracted entities into one of four labels: A: correct and relevant, B: correct and somewhat relevant, C: correct but irrelevant, and D: incorrect. Results show that $29.4\%$ , $13.7\%$ , $30.0\%$ , and $26.9\%$ are tagged with A, B, C, and D, respectively, which shows that there is a large amount of incorrect and irrelevant entities.
raw_evidence: Despite its usefulness, linked entities extracted from ELS's have issues because of low precision rates BIBREF11 and design challenges in training datasets BIBREF12 . These issues can be summarized into two parts: ambiguity and coarseness.
Second, the linked entities may also be too common to be considered an entity. This may introduce errors and irrelevance to the summary. In the example, “Wednesday” is erroneous because it is wrongly linked to the entity “Wednesday Night Baseball”. Also, “swap” is irrelevant because although it is linked correctly to the entity “Trade (Sports)”, it is too common and irrelevant when generating the summaries. In our experimental data, we randomly select 100 data instances and tag the correctness and relevance of extracted entities into one of four labels: A: correct and relevant, B: correct and somewhat relevant, C: correct but irrelevant, and D: incorrect. Results show that $29.4\%$ , $13.7\%$ , $30.0\%$ , and $26.9\%$ are tagged with A, B, C, and D, respectively, which shows that there is a large amount of incorrect and irrelevant entities.

Answers:
Linked entities may be ambiguous or too common
linked entities extracted from ELS's have issues because of low precision rates BIBREF11 and design challenges in training datasets BIBREF12 . These issues can be summarized into two parts: ambiguity and coarseness., the linked entities may also be too common to be considered an entity.